Accuracy is an important factor to consider when evaluating the performance of a manipulator. The accuracy of a manipulator is determined by its ability to accurately move and position objects in a precise manner. This research paper aims to evaluate the performance of different methods for the kinematic analysis of manipulators. The study employs four distinct techniques, namely mathematical modeling using the closed form solutions method, roboanalyzer, Peter Corke toolbox, and particle swarm optimization, to perform kinematic analysis for manipulators. The KUKA industrial manipulator is used as an illustrative case study in this research due to its widespread use in various industrial applications in addition to its high precision and stability. Its wide usage in the industry makes the results of this research highly relevant and allows for a thorough evaluation of the performance of the different methods being studied. Furthermore, understanding the kinematic analysis of the manipulator can also help in improving the performance and increasing the efficiency of the robot in different tasks. This paper conducts a comparison of the accuracy of the four methods, and the results indicate that particle swarm optimization is the most accurate method. The RoboAnalyzer approach achieved the fastest execution time.
Citation: Mohamed S. Elhadidy, Waleed S. Abdalla, Alaa A. Abdelrahman, S. Elnaggar, Mostafa Elhosseini. Assessing the accuracy and efficiency of kinematic analysis tools for six-DOF industrial manipulators: The KUKA robot case study[J]. AIMS Mathematics, 2024, 9(6): 13944-13979. doi: 10.3934/math.2024678
[1] | Muhammad Farman, Ali Akgül, Kottakkaran Sooppy Nisar, Dilshad Ahmad, Aqeel Ahmad, Sarfaraz Kamangar, C Ahamed Saleel . Epidemiological analysis of fractional order COVID-19 model with Mittag-Leffler kernel. AIMS Mathematics, 2022, 7(1): 756-783. doi: 10.3934/math.2022046 |
[2] | Minghung Lin, Yiyou Hou, Maryam A. Al-Towailb, Hassan Saberi-Nik . The global attractive sets and synchronization of a fractional-order complex dynamical system. AIMS Mathematics, 2023, 8(2): 3523-3541. doi: 10.3934/math.2023179 |
[3] | Rahat Zarin, Amir Khan, Aurangzeb, Ali Akgül, Esra Karatas Akgül, Usa Wannasingha Humphries . Fractional modeling of COVID-19 pandemic model with real data from Pakistan under the ABC operator. AIMS Mathematics, 2022, 7(9): 15939-15964. doi: 10.3934/math.2022872 |
[4] | Pratap Anbalagan, Evren Hincal, Raja Ramachandran, Dumitru Baleanu, Jinde Cao, Michal Niezabitowski . A Razumikhin approach to stability and synchronization criteria for fractional order time delayed gene regulatory networks. AIMS Mathematics, 2021, 6(5): 4526-4555. doi: 10.3934/math.2021268 |
[5] | Muhammad Farman, Ali Akgül, Sameh Askar, Thongchai Botmart, Aqeel Ahmad, Hijaz Ahmad . Modeling and analysis of fractional order Zika model. AIMS Mathematics, 2022, 7(3): 3912-3938. doi: 10.3934/math.2022216 |
[6] | Jingfeng Wang, Chuanzhi Bai . Global Mittag-Leffler stability of Caputo fractional-order fuzzy inertial neural networks with delay. AIMS Mathematics, 2023, 8(10): 22538-22552. doi: 10.3934/math.20231148 |
[7] | Yuehong Zhang, Zhiying Li, Wangdong Jiang, Wei Liu . The stability of anti-periodic solutions for fractional-order inertial BAM neural networks with time-delays. AIMS Mathematics, 2023, 8(3): 6176-6190. doi: 10.3934/math.2023312 |
[8] | Weerawat Sudsutad, Jutarat Kongson, Chatthai Thaiprayoon, Nantapat Jarasthitikulchai, Marisa Kaewsuwan . A generalized Gronwall inequality via ψ-Hilfer proportional fractional operators and its applications to nonlocal Cauchy-type system. AIMS Mathematics, 2024, 9(9): 24443-24479. doi: 10.3934/math.20241191 |
[9] | Rana Safdar Ali, Saba Batool, Shahid Mubeen, Asad Ali, Gauhar Rahman, Muhammad Samraiz, Kottakkaran Sooppy Nisar, Roshan Noor Mohamed . On generalized fractional integral operator associated with generalized Bessel-Maitland function. AIMS Mathematics, 2022, 7(2): 3027-3046. doi: 10.3934/math.2022167 |
[10] | Yonghong Liu, Ghulam Farid, Dina Abuzaid, Hafsa Yasmeen . On boundedness of fractional integral operators via several kinds of convex functions. AIMS Mathematics, 2022, 7(10): 19167-19179. doi: 10.3934/math.20221052 |
Accuracy is an important factor to consider when evaluating the performance of a manipulator. The accuracy of a manipulator is determined by its ability to accurately move and position objects in a precise manner. This research paper aims to evaluate the performance of different methods for the kinematic analysis of manipulators. The study employs four distinct techniques, namely mathematical modeling using the closed form solutions method, roboanalyzer, Peter Corke toolbox, and particle swarm optimization, to perform kinematic analysis for manipulators. The KUKA industrial manipulator is used as an illustrative case study in this research due to its widespread use in various industrial applications in addition to its high precision and stability. Its wide usage in the industry makes the results of this research highly relevant and allows for a thorough evaluation of the performance of the different methods being studied. Furthermore, understanding the kinematic analysis of the manipulator can also help in improving the performance and increasing the efficiency of the robot in different tasks. This paper conducts a comparison of the accuracy of the four methods, and the results indicate that particle swarm optimization is the most accurate method. The RoboAnalyzer approach achieved the fastest execution time.
As we all know, the study of variable exponent function space inspired by nonlinear elasticity theory and nonstandard growth differential equations is one of the key contents of harmonic analysis in the past three decades, attracting extensive attention from many scholars. In [19], the theory of function spaces with variable exponent was progressed since some elementary properties were established by Kováčik and Rákosník, and they studied many basic properties of variable exponent Lebesgue spaces and Sobolev spaces on Rn. Later, the Lebesgue spaces with variable exponent Lp(⋅)(Rn) were extensively investigated; see [7,8,22]. In [14], Izuki first introduced the Herz spaces with variable exponent ˙Kα,qp(⋅)(Rn), which are generalizations of the Herz spaces ˙Kα,qp(Rn), and considered the boundedness of commutators of fractional integrals on Herz spaces with variable exponent. In [13], Izuki introduced the Herz-Morrey spaces with variable exponent M˙Kα,λq,p(⋅)(Rn), which are generalizations of the Herz-Morrey spaces M˙Kα,λq,p(Rn), and studied the boundedness of vector valued sublinear operators on Herz-Morrey spaces with variable exponent M˙Kα,λq,p(⋅)(Rn). On the other hand, in the study of boundary value problems for the Laplace equation on Lipschitz domains, the classical theory of Muckenhoupt weights is a powerful tool in harmonic analysis; see [21]. Generalized Muckenhoupt weights with variable exponent have been intensively studied; see [4,5].
In [11], Hardy defined the classical Hardy operators as:
P(f)(x):=1x∫x0f(t)dt,x>0. | (1.1) |
In [6], Christ and Grafakos defined the n−dimensional Hardy operators as:
H(f)(x):=1|x|n∫|t|<|x|f(t)dt,x∈Rn∖{0}, | (1.2) |
and established the boundedness of P(f)(x) in Lp(Rn), getting the best constants.
In [9], under the condition of 0≤β<n and |x|=√∑ni=1x2i, Fu et al. defined the n−dimensional fractional Hardy operators and its adjoint operators as:
Hβf(x):=1|x|n−β∫|t|<|x|f(t)dt,H∗βf(x):=∫|t|≥|x|f(t)|t|n−βdt,x∈Rn∖{0}, | (1.3) |
and established the boundedness of their commutators in Lebesgue spaces and homogeneous Herz spaces.
Let L1loc(Rn) be the collection of all locally integrable functions on Rn. Given a function b∈L1loc(Rn) and m∈N, Wang et al. [23] defined the mth order commutators of n−dimensional fractional Hardy operators and adjoint operators as:
Hmβ,bf(x):=1|x|n−β∫|t|<|x|(b(x)−b(t))mf(t)dt | (1.4) |
and
H∗mβ,bf(x):=∫|t|≥|x|(b(x)−b(t))mf(t)|t|n−βdt,x∈Rn∖{0}. | (1.5) |
Obviously, when m=0, H0β,b=Hβ, H∗0β,b=H∗β, and when m=1, H1β,b=Hβ,b, H∗1β,b=H∗β,b. More important results with regard to these commutators, see [20,26,27].
Due to the need of future calculation in this paper, let 0<β<n, and the fractional integral operator Iβ is defined as:
Iβ(f)(x):=∫Rnf(y)|x−y|n−βdy,x∈Rn. | (1.6) |
Let 0≤β<n and f∈L1loc(Rn), and the fractional maximal operator Mβ is defined as:
Mβf(x):=supx∈B1|B|1−βn∫B|f(y)|dy,x∈Rn, | (1.7) |
where the supremum is taken over all balls B⊂Rn containing x. When β=0, we simply write M instead of M0, which is exactly the Hardy-Littlewood maximal function.
Let f∈L1loc(Rn) and BMO(Rn) consist of all f∈L1loc(Rn) with BMO(Rn)<∞. b is a bounded mean oscillation function if ‖b‖BMO<∞, and the ‖b‖BMO is defined as follow:
‖b‖BMO:=supB∫B|b(x)−bB|dx, | (1.8) |
where the supremum is taken all over the balls B∈Rn and bB:=|B|−1∫Bb(y)dy. For a comprehensive review of the bounded mean oscillation (BMO) space, please see the book [10].
Recently, Muhammad Asim et al. established the estimates of fractional Hardy operators on weighted variable exponent Morrey-Herz spaces in [1]. Amjad Hussain et al. established the boundedness of the commutators of the Fractional Hardy operators on weighted variable Herz-Morrey spaces in [12]. Motivated by the mentioned work, in this paper, we will give the boundedness of the mth order commutators of n−dimensional fractional Hardy operators Hmβ,b and its adjoint operators H∗mβ,b on weighted variable exponent Morrey-Herz space M˙Kα,λq,p(⋅)(ω).
The paper is organized as follows. In Section 2, we provide some preliminary knowledge. The main results and their proofs are given in Section 3. In Section 4, we provide the conclusion of this paper. Throughout this paper, we use the following symbols and notations:
(1) For a constant R>0 and a point x∈Rn, we write B(x,R):={y∈Rn:|x−y|<R}.
(2) For any measurable set E⊂Rn, |E| denotes the Lebesgue measure, and χE means the characteristic function.
(3) Given k∈Z, we write Bk:=¯B(0,2k)={x∈Rn:|x|≤2k}.
(4) We define a family {Ak}∞k=−∞ by Ak:=Bk∖Bk−1={x∈Rn:2k−1<|x|≤2k}. Moreover χk denotes the characteristic function of Ak, namely, χk:=χAk.
(5) For any index 1<p(x)<∞, p′(x) denotes its conjugate index, namely, 1p(x)+1p′(x)=1.
(6) If there exists a positive constant C independent of the main parameters such that A≤CB, then we write A≲. Additionally, A\approx B means that both A\lesssim B and B\lesssim A hold.
\mathbf{Definition\; 2.1.} ([7]) Let p(\cdot): \mathbb{R}^{n}\rightarrow [1, \infty) be a measurable function.
(ⅰ) The Lebesgue space with variable exponent L^{p(\cdot)}(\mathbb{R}^{n}) is defined by
L^{p(\cdot)}(\mathbb{R}^{n}): = \Big\{f\; \mathrm{is\; measurable\; function}:\int_{\mathbb{R}^{n}}\Big(\frac{|f(x)|}{\lambda}\Big)^{p(x)}\mathrm{d}x < \infty \; \mathrm{for\; some\; constant}\; \lambda > 0\Big\}. |
(ⅱ) The spaces with variable exponent L_{\mathrm{loc}}^{p(\cdot)}(\mathbb{R}^{n}) are defined by
L^{p(\cdot)}_{\mathrm{loc}}(\mathbb{R}^{n}): = \{f\; \mathrm{is\; measurable\; function}:f\in L^{p(\cdot)}(K) \mathrm{\; for\; all\; compact\; subsets\; } K\subset \mathbb{R}^{n}\}. |
The Lebesgue space L^{p(\cdot)}(\mathbb{R}^{n}) is a Banach space with the norm defined by
\|f\|_{L^{p(\cdot)}(\mathbb{R}^{n})}: = \inf\Big\{\lambda > 0:\int_{\mathbb{R}^{n}}\Big(\frac{|f(x)|}{\lambda}\Big)^{p(x)}\mathrm{d}x\leq 1 \Big\}. |
\mathbf{Definition\; 2.2.} ([7]) (ⅰ) The set \mathcal{P}(\mathbb{R}^{n}) consists of all measurable functions p(\cdot): \mathbb{R}^{n}\rightarrow [1, \infty) satisfying
1 < p_{-}\leq p(x)\leq p_{+} < \infty, |
where
p_{-}: = \mathrm{essinf}\{p(x): x\in \mathbb{R}^{n}\} > 1,\; \; \; p_{+}: = \mathrm{esssup}\{p(x): x\in \mathbb{R}^{n}\} < \infty. |
(ⅱ) The set \mathcal{B}(\mathbb{R}^{n}) consists of all measurable function p(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) satisfying that the Hardy-Littlewood maximal operator M is bounded on L^{p(\cdot)}(\mathbb{R}^{n}) .
\mathbf{Definition\; 2.3.} ([7]) Suppose that p(\cdot) is a real-valued function on \mathbb{R}^{n} . We say that
(ⅰ) \mathcal{C}_{\mathrm{loc}}^{\mathrm{log}}(\mathbb{R}^{n}) is the set of all local log-Hölder continuous functions p(\cdot) satisfying
\begin{align} |p(x)-p(y)|\leq -\frac{C}{\mathrm{log}(|x-y|)},\; |x-y|\leq\frac{1}{2}, \; \; x,y\in \mathbb{R}^{n}. \end{align} | (2.1) |
(ⅱ) \mathcal{C}_{0}^{\mathrm{log}}(\mathbb{R}^{n}) is the set of all local log-Hölder continuous functions p(\cdot) satisfying at origin
\begin{align} |p(x)-p_{0}|\leq \frac{C}{\mathrm{log}(e+\frac{1}{|x|})},\; \; x\in \mathbb{R}^{n}. \end{align} | (2.2) |
(ⅲ) \mathcal{C}_{\mathrm{\infty}}^{\mathrm{log}}(\mathbb{R}^{n}) is the set of all local log-Hölder continuous functions satisfying at infinity
\begin{align} |p(x)-p_{\infty}|\leq \frac{C_{\infty}}{\mathrm{log}(e+|x|)},\; \; x\in \mathbb{R}^{n}. \end{align} | (2.3) |
(ⅳ) \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) = \mathcal{C}_{\mathrm{\infty}}^{\mathrm{log}}(\mathbb{R}^{n})\cap \mathcal{C}_{\mathrm{loc}}^{\mathrm{log}}(\mathbb{R}^{n}) denotes the set of all global log-Hölder continuous functions p(\cdot) .
It was proved in [7] that if p(\cdot)\in \mathcal{P}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) , then the Hardy-Littlewood maximal operator M is bounded on L^{p(\cdot)}(\mathbb{R}^{n}) .
\mathbf{Definition\; 2.4.} ([21]) Given a non-negative, measure function \omega , for 1 < p < \infty , \omega\in A_{p} if
[\omega]_{A_{p}}: = \sup\limits_{B}\Big(\frac{1}{|B|}\int_{B}\omega(x)\mathrm{d}x\Big)\Big(\frac{1}{|B|}\int_{B}\omega(x)^{1-p^{\prime}}\mathrm{d}x\Big)^{p-1} < \infty, |
where the supremum is taken over all balls B\subset \mathbb{R}^{n} . Especially, we say \omega\in A_{1} if
[\omega]_{A_{1}}: = \sup\limits_{B}\frac{\frac{1}{|B|}\int_{B}\omega(x)\mathrm{d}x}{\mathrm{essinf}\{\omega(x): x\in B\}} < \infty. |
These weights characterize the weighted norm inequalities for the Hardy-Littlewood maximal operator, that is, \omega\in A_{p} , 1 < p < \infty , if and only if M:L^{p}(\omega)\rightarrow L^{p}(\omega) .
\mathbf{Definition\; 2.5.} ([15]) Suppose that p(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) . A weight \omega is in the class A_{p(\cdot)} if
\begin{align} \sup\limits_{B:\mathrm{ball}}|B|^{-1}\|\omega^{\frac{1}{p(\cdot)}}\chi_{B}\|_{L^{p(\cdot)}}\|\omega^{-\frac{1}{p(\cdot)}}\chi_{B}\|_{L^{p^{\prime}(\cdot)}} < \infty. \end{align} | (2.4) |
Obviously, if p(\cdot) = p, 1 < p < \infty , then the above definition reduces to the classical Muckenhoupt A_{p} class.
From [15], if p(\cdot), q(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) , and p(\cdot)\leq q(\cdot) , then A_{1}\subset A_{p(\cdot)}\subset A_{q(\cdot)} .
\mathbf{Definition\; 2.6.} ([15]) Let 0 < \beta < n and p_{1}(\cdot), p_{2}(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) such that \frac{1}{p_{2}(x)} = \frac{1}{p_{1}(x)}-\frac{\beta}{n} . A weight \omega is said to be an A(p_{1}(\cdot), p_{2}(\cdot)) weight if
\begin{align} \|\chi_{B}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\|\chi_{B}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})^{\prime}}\leq C|B|^{1-\frac{\beta}{n}}. \end{align} | (2.5) |
\mathbf{Definition\; 2.7.} ([25]) Let p(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) and \omega\in A_{p(\cdot)} . The weighted variable exponent Lebesgue space L^{p(\cdot)}(\omega) denotes the set of all complex-valued measurable functions f satisfying
L^{p(\cdot)}(\omega): = \{f:f\omega^{\frac{1}{p(\cdot)}}\in L^{p(\cdot)}(\mathbb{R}^{n})\}. |
This is a Banach space equipped with the norm
\|f\|_{L^{p(\cdot)}(\omega)}: = \|f\omega^{\frac{1}{p(\cdot)}}\|_{L^{p(\cdot)}(\mathbb{R}^{n})}. |
\mathbf{Definition\; 2.8.} ([1]) Let \omega be a weight on \mathbb{R}^{n} , 0\leq \lambda < \infty , 0 < q < \infty , p(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) , and \alpha(\cdot): \mathbb{R}^{n}\rightarrow \mathbb{R} with \alpha(\cdot)\in L^{\infty}(\mathbb{R}^{n}) . The weighted variable exponent Morrey-Herz space \mathrm{M\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot), \lambda}(\omega) is the set of all measurable functions f given by
\mathrm{M\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot), \lambda}(\omega): = \{f\in L_{\mathrm{loc}}^{p(\cdot)}(\mathbb{R}^{n}\backslash\{0\}, \omega): \|f\|_{\mathrm{M\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot), \lambda}(\omega)} < \infty \}, |
where
\|f\|_{\mathrm{M\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot), \lambda}(\omega)}: = \sup\limits_{k_{0}\in \mathbb{Z}}2^{-k_{0}\lambda}\Big\{\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(\cdot) q}\|f\chi_{k}\|_{L^{p(\cdot)}(\omega)}^{q} \Big\}^{\frac{1}{q}}. |
It is noted that \mathrm{M\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot), 0}(\omega) = \mathrm{\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot)}(\omega) is the variable exponent weighted Herz space defined in [2].
\mathbf{Definition\; 2.9.} ([15]) Let \mathcal{M} be the set of all complex-valued measurable functions defined on \mathbb{R}^{n} and X be a linear subspace of \mathcal{M} .
(1) The space X is said to be a Banach function space if there exists a function \|\cdot\|_{X}:\mathcal{M}\rightarrow [0, \infty] satisfying the following properties: Let f, g, f_{j}\in\mathcal{M}\; (j = 1, 2, \ldots) . Then
(a) f\in X holds if and only if \|f\|_{X} < \infty .
(b) Norm property:
ⅰ. Positivity: \|f\|_{X}\geq 0 .
ⅱ. Strict positivity: \|f\|_{X} = 0 holds if and only if f(x) = 0 for almost every x\in \mathbb{R}^{n} .
ⅲ. Homogeneity: \|\lambda f\|_{X} = |\lambda|\cdot\|f\|_{X} holds for all \lambda\in\mathbb{C} .
ⅳ. Triangle inequality: \|f+g\|_{X}\leq \|f\|_{X}+\|g\|_{X} .
(c) Symmetry: \|f\|_{X} = \||f|\|_{X} .
(d) Lattice property: If 0\leq g(x)\leq f(x) for almost every x\in \mathbb{R}^{n} , then \|g\|_{X}\leq\|f\|_{X} .
(e) Fatou property: If 0\leq f_{j}(x)\leq f_{j+1}(x) for all j , and f_{j}(x)\rightarrow f(x) as j\rightarrow \infty for almost every x\in \mathbb{R}^{n} , then \lim\limits_{j\rightarrow \infty}\|f_{j}\|_{X} = \|f\|_{X} .
(f) For every measurable set F\subset \mathbb{R}^{n} such that |F| < \infty , \|\chi_{F}\|_{X} is finite. Additionally, there exists a constant C_{F} > 0 depending only on F so that \int_{F}|h(x)|\mathrm{d}x\leq C_{F}\|h\|_{X} holds for all h\in X .
(2) Suppose that X is a Banach function space equipped with a norm \|\cdot\|_{X} . The associated space X^{\prime} is defined by
X^{\prime}: = \{f\in \mathcal{M}:\|f\|_{X^{\prime}} < \infty\}, |
where
\|f\|_{X^{\prime}}: = \sup\limits_{g}\Big\{\Big|\int_{\mathbb{R}^{n}}f(x)g(x)\mathrm{d}x\Big|:\|g\|_{X}\leq 1 \Big\}. |
\mathbf{Lemma\; 2.1.} ([3]) Let X be a Banach function space, and then we have the following:
(ⅰ) The associated space X^{\prime} is also a Banach function space.
(ⅱ) \|\cdot\|_{(X^{\prime})^{\prime}} and \|\cdot\|_{X} are equivalent.
(ⅲ) If g\in X and f\in X^{\prime} , then
\begin{align} \int_{\mathbb{R}^{n}}|f(x)g(x)|\mathrm{d}x\leq \|f\|_{X}\|g\|_{X^{\prime}} \end{align} | (2.6) |
is the generalized Hölder inequality.
\mathbf{Lemma\; 2.2.} ([15]) If X is a Banach function space, then we have, for all balls B ,
\begin{align} 1\leq |B|^{-1}\|\chi_{B}\|_{X}\|\chi_{B}\|_{X^{\prime}}. \end{align} | (2.7) |
\mathbf{Lemma\; 2.3.} ([16]) Let X be a Banach function space. Suppose that the Hardy-Littlewood maximal operator M is weakly bounded on X , that is,
\|\chi_{\{Mf > \lambda\}}\|_{X}\lesssim \lambda^{-1}\|f\|_{X} |
is true for all f\in X and all \lambda > 0 . Then, we have
\begin{align} \sup\limits_{B:\mathrm{ball}}\frac{1}{|B|}\|\chi_{B}\|_{X}\|\chi_{B}\|_{X^{\prime}} < \infty. \end{align} | (2.8) |
\mathbf{Lemma\; 2.4.} ([15]) Given a function W such that 0 < W(x) < \infty for almost every x\in \mathbb{R}^{n} , W\in X_{\mathrm{loc}}(\mathbb{R}^{n}) and W^{-1}\in (X^{\prime})_{\mathrm{loc}}(\mathbb{R}^{n}) ,
(ⅰ) X(\mathbb{R}^{n}, W) is Banach function space equipped with the norm
\begin{align} \|f\|_{X(\mathbb{R}^{n}, W)}: = \|fW\|_{X}, \end{align} | (2.9) |
where
\begin{align} X(\mathbb{R}^{n}, W): = \{f\in\mathcal{M}: fW\in X\}. \end{align} | (2.10) |
(ⅱ) The associated space X^{\prime}(\mathbb{R}^{n}, W^{-1}) of X(\mathbb{R}^{n}, W) is also a Banach function space.
\mathbf{Lemma\; 2.5.} ([15]) Let X be a Banach function space and M be bounded on X^{\prime} . Then, there exists a constant \delta\in(0, 1) for all B\subset \mathbb{R}^{n} and E\subset B ,
\begin{align} \frac{\|\chi_{_{E}}\|_{X}}{\|\chi_{_{B}}\|_{X}}\leq \Big( \frac{|E|}{|B|}\Big)^{^{\delta}}. \end{align} | (2.11) |
The paper [19] shows that L^{p(\cdot)}(\mathbb{R}^{n}) is a Banach function space and the associated space L^{p^{\prime}(\cdot)}(\mathbb{R}^{n}) with equivalent norm.
\mathbf{Remark\; 2.6.} ([1]) Let p(\cdot)\in\mathcal{P}(\mathbb{R}^{n}) , and by comparing the L^{p(\cdot)}(\omega^{p(\cdot)}) and L^{p^{\prime}(\cdot)}(\omega^{-p^{\prime}(\cdot)}) with the definition of X(\mathbb{R}^{n}, W) , we have the following:
(1) If we take W = \omega and X = L^{p(\cdot)}(\mathbb{R}^{n}) , then we get L^{p(\cdot)}(\mathbb{R}^{n}, \omega) = L^{p(\cdot)}(\omega^{p(\cdot)}) .
(2) If we consider W = \omega^{-1} and X = L^{p^{\prime}(\cdot)}(\mathbb{R}^{n}) , then we get L^{p^{\prime}(\cdot)}(\mathbb{R}^{n}, \omega^{-1}) = L^{p^{\prime}(\cdot)}(\omega^{-p^{\prime}(\cdot)}) .
By virtue of Lemma 2.4, we get
(L^{p(\cdot)}(\mathbb{R}^{n}, \omega))^{\prime} = (L^{p(\cdot)}(\omega^{p(\cdot)}))^{\prime} = L^{p^{\prime}(\cdot)}(\omega^{-p^{\prime}(\cdot)}) = L^{p^{\prime}(\cdot)}(\mathbb{R}^{n}, \omega^{-1}). |
\mathbf{Lemma\; 2.7.} ([17]) Let p(\cdot)\in\mathcal{P}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) be a log-Hölder continuous function both at infinity and at origin, if \omega^{p_{_{2}}(\cdot)}\in A_{p_{_{2}}(\cdot)} implies \omega^{-p^{\prime}_{_{2}}(\cdot)}\in A_{p^{\prime}_{_{2}}(\cdot)} . Thus, the Hardy-Littlewood operator is bounded on L^{p^{\prime}_{_{2}}(\cdot)}(\omega^{-p^{\prime}_{_{2}}(\cdot)}) , and there exist constants \delta_{1}, \delta_{2}\in (0, 1) such that
\begin{align} \frac{\|\chi_{E}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}}{\|\chi_{B}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}} = \frac{\|\chi_{E}\|_{(L^{p^{\prime}_{2}(\cdot)}(\omega^{-p^{\prime}_{2}(\cdot)}))^{\prime}}}{\|\chi_{B}\|_{(L^{p^{\prime}_{2}(\cdot)}(\omega^{-p^{\prime}_{2}(\cdot)}))^{\prime}}} \leq C\Big(\frac{|E|}{|B|}\Big)^{\delta_{1}}, \end{align} | (2.12) |
and
\begin{align} \frac{\|\chi_{E}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}}{\|\chi_{B}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}}\leq C\Big(\frac{|E|}{|B|}\Big)^{\delta_{2}}, \end{align} | (2.13) |
for all balls B\subset \mathbb{R}^{n} and all measurable sets E\subset B .
\mathbf{Lemma\; 2.8.} ([15]) Let p_{1}(\cdot)\in \mathcal{P}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) and 0 < \beta < \frac{n}{p_{1}^{+}} . Define p_{2}(\cdot) by \frac{1}{p_{1}(x)}-\frac{1}{p_{2}(\cdot)} = \frac{\beta}{n} . If \omega\in A(p_{1}(\cdot), p_{2}(\cdot)) , then I_{\beta} is bounded from L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}) to L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}) .
\mathbf{Lemma\; 2.9.} ([24, Corollary 3.11]) Let b\in \mathrm{BMO}(\mathbb{R}^{n}), m\in \mathbb{N} , and k, j\in \mathbb{Z} with k > j . Then, we have
\begin{align} C^{-1}\|b\|_{\mathrm{BMO}(\mathbb{R}^{n})}^{m}\leq \sup\limits_{B}\frac{1}{\|\chi_{B}\|_{L^{p(\cdot)}(\omega)}}\|(b-b_{B})^{m}\chi_{B}\|_{L^{p(\cdot)}(\omega)}\leq C\|b\|_{\mathrm{BMO}(\mathbb{R}^{n})}^{m}. \end{align} | (2.14) |
\begin{align} \|(b-b_{B_{j}})^{m}\chi_{B_{k}}\|_{L^{p(\cdot)}(\omega)}\leq C(k-j)^{m}\|b\|_{\mathrm{BMO}(\mathbb{R}^{n})}^{m}\|\chi_{B_{k}}\|_{L^{p(\cdot)}(\omega)}. \end{align} | (2.15) |
\mathbf{Proposition\; 3.1.} ([12] Let q(\cdot)\in \mathcal{P}(\mathbb{R}^{n}) , 0 < p < \infty , and 0\leq \lambda < \infty . If \alpha(\cdot)\in L^{\infty}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) , then
\begin{align*} \|f\|_{\mathrm{M\dot{K}}_{p, q(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{q(\cdot)})}^{p}& = \sup\limits_{k_{0}\in \mathbb{Z}}2^{-k_{0}\lambda p}\sum\limits_{j = -\infty}\limits^{k_{0}}2^{j\alpha(\cdot)p}\|f\chi_{j}\|^{p}_{L^{q(\cdot)}(\omega^{q(\cdot)})}\\ &\leq \max\Big\{\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda p}\Big(\sum\limits_{j = -\infty}\limits^{k_{0}}2^{j\alpha(0)p}\|f\chi_{j}\|^{p}_{L^{q(\cdot)}(\omega^{q(\cdot)})}\Big), \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}\Big(2^{-k_{0}\lambda p}\Big(\sum\limits_{j = -\infty}\limits^{-1}2^{j\alpha(0)p}\|f\chi_{j}\|^{p}_{L^{q(\cdot)}(\omega^{q(\cdot)})}\Big)\\ &+2^{-k_{0}\lambda p}\Big(\sum\limits_{j = 0}\limits^{k_{0}}2^{j\alpha(\infty)p}\|f\chi_{j}\|^{p}_{L^{q(\cdot)}(\omega^{q(\cdot)})}\Big)\Big)\Big\}. \end{align*} |
\mathbf{Theorem\; 3.1.} Let 0 < q_{1}\leq q_{2} < \infty , p_{2}(\cdot)\in\mathcal{P}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) and p_{1}(\cdot) be such that \frac{1}{p_{2}(\cdot)} = \frac{1}{p_{1}(\cdot)}-\frac{\beta}{n} . Also, let \omega^{p_{2}(\cdot)}\in A_{1} , b\in \mathrm{BMO}(\mathbb{R}^{n}) , \lambda > 0 and \alpha(\cdot)\in L^{\infty}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) be log-Hölder continuous at the origin, with \alpha(0)\leq \alpha(\infty) < \lambda+n\delta_{2}-\beta , where \delta_{2}\in(0, 1) is the constant appearing in (2.13). Then,
\begin{align} \|\mathcal{H}^{m}_{_{\beta,b}}f\|_{\mathrm{M\dot{K}}_{q_{2}, p_{2}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{2}(\cdot)})}\lesssim \|b\|^{m}_{\mathrm{BMO}}\|f\|_{\mathrm{M\dot{K}}_{q_{1}, p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. \end{align} | (3.1) |
Proof. For arbitrary f\in \mathrm{M\dot{K}}_{q_{1}, p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)}) , let f_{j} = f\cdot\chi_{j} = f\cdot\chi_{A_{j}} for every j\in \mathbb{Z} , and then
\begin{align} f(x) = \sum\limits_{j = -\infty}^{\infty}f(x)\cdot\chi_{j}(x) = \sum\limits_{j = -\infty}^{\infty}f_{j}(x). \end{align} | (3.2) |
By the inequality of C_{p} , it is not difficult to see that
\begin{align*} |\mathcal{H}^{m}_{_{\beta,b}}f(x)\chi_{k}(x)|&\leq \frac{1}{|x|^{n-\beta}}\int_{|t| < |x|}|b(x)-b(t)|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq \frac{1}{|x|^{n-\beta}}\int_{B(0,|x|)}|b(x)-b(t)|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq \frac{1}{|x|^{n-\beta}}\int_{B_{k}}|b(x)-b(t)|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\int_{A_{j}}|b(x)-b(t)|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\lesssim 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\int_{A_{j}}|b(x)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\; \; \; +2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\int_{A_{j}}|b(t)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x) = E_{1}+E_{2}. \end{align*} | (3.3) |
For E_{1} , by the generalized Hölder inequality, we have
\begin{align*} E_{1}& = 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\int_{A_{j}}|b(x)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}|b(x)-b_{A_{j}}|^{m}\cdot\chi_{k}(x)\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}. \end{align*} | (3.4) |
By taking the (L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})) -norm on both sides of (3.4) and using (2.15) of Lemma 2.9, we get
\begin{align*} \|E_{1}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))} &\leq 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\||b(x)-b_{A_{j}}|^{m}\cdot\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\\ &\lesssim 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}. \end{align*} | (3.5) |
For E_{2} , by the generalized Hölder inequality, we have
\begin{align*} E_{2}& = 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\int_{A_{j}}|b(t)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\||b(t)-b_{A_{j}}|^{m}\cdot\chi_{j}(x)\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\cdot\chi_{k}(x). \end{align*} | (3.6) |
By taking the (L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})) -norm on both sides of (3.6) and using (2.14) of Lemma 2.9, we get
\begin{align*} \|E_{2}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))} &\leq 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\||b(x)-b_{A_{j}}|^{m}\cdot\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\\ &\lesssim 2^{-k(n-\beta)}\sum\limits_{j = -\infty}^{k}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}. \end{align*} | (3.7) |
Hence, from inequalities (3.3), (3.5) and (3.7), we get
\begin{align*} &\|\mathcal{H}^{m}_{_{\beta,b}}f(x)\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\\ &\lesssim 2^{-k(n-\beta)}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\Big\{\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\\ &+\sum\limits_{j = -\infty}^{k}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\Big\}\\ &\lesssim 2^{-k(n-\beta)}\|b\|_{\mathrm{BMO}}^{m}\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}. & \end{align*} | (3.8) |
By virtue of Lemma 2.5, we have
\begin{align} \frac{\|\chi_{B_{k}}\|_{X}}{\|\chi_{k}\|_{X}}\leq (\frac{|B_{k}|}{|A_{k}|})^{\delta} = C\; \Longrightarrow\; \|\chi_{B_{k}}\|_{X}\leq C\|\chi_{k}\|_{X}. \end{align} | (3.9) |
Note that \|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})} \leq\|\chi_{B_{j}}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})} and \chi_{B_{j}}(x)\lesssim 2^{-j\beta}I_{\beta}(\chi_{B_{j}}) (see [18, p. 350]). By applying (2.8), (3.9) and Lemma 2.8, we obtain
\begin{align*} \|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}&\leq\|\chi_{B_{j}}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\lesssim 2^{-j\beta}\|I_{\beta}(\chi_{B_{j}})\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\lesssim 2^{-j\beta}\|\chi_{B_{j}}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\lesssim 2^{-j\beta}\|\chi_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})} \lesssim 2^{j(n-\beta)}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}^{-1}. & \end{align*} | (3.10) |
By virtue of (2.7) and (2.8), combining (2.13) and (3.10), we have
\begin{align*} &2^{k(\beta-n)}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|\chi_{k}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\; \; \; \; = 2^{k\beta}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}} 2^{-kn}\|\chi_{k}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\; \; \; \; \lesssim 2^{k\beta}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}} \|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}^{-1}\\ &\; \; \; \; = 2^{k\beta} \|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}} \|\chi_{j}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}^{-1}\frac{ \|\chi_{j}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}}{ \|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}}\\ &\; \; \; \; \lesssim 2^{k\beta}2^{n\delta_{2}(j-k)} \|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}} \|\chi_{j}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}^{-1}\\ &\; \; \; \; \lesssim 2^{k\beta}2^{n\delta_{2}(j-k)}2^{j(n-\beta)} \|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}^{-1} \|\chi_{j}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}^{-1}\\ &\; \; \; \; = 2^{k\beta}2^{n\delta_{2}(j-k)}2^{-j\beta} \Big(2^{-jn}\|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\|\chi_{j}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))^{\prime}}\Big)^{-1}\\ &\; \; \; \; \lesssim 2^{(\beta-n\delta_{2})(k-j)}. & \end{align*} | (3.11) |
Hence by virtue of (3.8) and (3.11), we have
\begin{align*} \|\mathcal{H}^{m}_{_{\beta,b}}f(x)\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\lesssim \|b\|_{\mathrm{BMO}}^{m}\sum\limits_{j = -\infty}^{k}(k-j)^{m}2^{(\beta-n\delta_{2})(k-j)}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}. & \end{align*} | (3.12) |
In order to estimate \|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})} , we consider two cases as below.
Case 1: For j < 0 , we get
\begin{align*} \|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}& = 2^{-j\alpha(0)}\Big(2^{j\alpha(0) q_{1}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{q_{1}}\Big)^{\frac{1}{q_{1}}}\\ &\leq 2^{-j\alpha(0)}\Big(\sum\limits_{i = -\infty}^{j}2^{i\alpha(0) q_{1}}\|f_{i}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{q_{1}}\Big)^{\frac{1}{q_{1}}}\\ & = 2^{j(\lambda-\alpha(0))}\Big\{2^{-j\lambda}\Big(\sum\limits_{i = -\infty}^{j}2^{i\alpha(\cdot) q_{1}}\|f_{i}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{q_{1}}\Big)^{\frac{1}{q_{1}}}\Big\}\\ &\lesssim 2^{j(\lambda-\alpha(0))}\|f\|_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. & \end{align*} | (3.13) |
Case 2: For j\geq0 , we get
\begin{align*} \|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}& = 2^{-j\alpha(\infty)}\Big(2^{j\alpha(\infty) q_{1}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{q_{1}}\Big)^{\frac{1}{q_{1}}}\\ &\leq 2^{-j\alpha(\infty)}\Big(\sum\limits_{i = -\infty}^{j}2^{i\alpha(\infty) q_{1}}\|f_{i}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{q_{1}}\Big)^{\frac{1}{q_{1}}}\\ & = 2^{j(\lambda-\alpha(\infty))}\Big\{2^{-j\lambda}\Big(\sum\limits_{i = -\infty}^{j}2^{i\alpha(\cdot) q_{1}}\|f_{i}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{q_{1}}\Big)^{\frac{1}{q_{1}}}\Big\}\\ &\lesssim 2^{j(\lambda-\alpha(\infty))}\|f\|_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. & \end{align*} | (3.14) |
Now, by virtue of the condition q_{1}\leq q_{2} and the definition of weighted variable exponent Morrey-Herz space along with the use of Proposition 3.1, we get
\begin{align*} \|\mathcal{H}^{m}_{_{\beta,b}}f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{2},p_{2}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{2}(\cdot)})}& = \sup\limits_{k_{0}\in \mathbb{Z}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(\cdot) q_{1}}\|\mathcal{H}^{m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\leq \max\Big\{\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})},\\ &\; \; \; \; \; \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\Big(\sum\limits_{k = -\infty}^{-1}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\; \; \; \; \; \; \; \; \; +\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\|\mathcal{H}^{m}_{\beta,b}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\Big)\Big\}\\ & = \max\{J_{1}, J_{2}+J_{3}\}, & \end{align*} | (3.15) |
where
\begin{align*} &J_{1} = \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})},\\ &J_{2} = \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{-1}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})},\\ &J_{3} = \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\|\mathcal{H}^{m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}. \end{align*} |
First, we estimate J_{1} . Since \alpha(0)\leq \alpha(\infty) < n\delta_{2}+\lambda-\beta , combining (3.12) and (3.13), we get
\begin{align*} J_{1}&\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{(\beta-n\delta_{2})(k-j)}\|f\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{(\beta-n\delta_{2})(k-j)}2^{j(\lambda-\alpha(0))}\|f\|_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}2^{(\beta-n\delta_{2})(k-j)}2^{j(\lambda-\alpha(0))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\lambda q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}2^{(j-k)(n\delta_{2}+\lambda-\beta-\alpha(0))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. \end{align*} |
The estimate of J_{2} is similar to that of J_{1} .
Lastly, we estimate J_{3} . Since \alpha(0)\leq \alpha(\infty) < n\delta_{2}+\lambda-\beta , combining (3.12) and (3.14), we get
\begin{align*} J_{3}&\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{(\beta-n\delta_{2})(k-j)}\|f\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{(\beta-n\delta_{2})(k-j)}2^{j(\lambda-\alpha(\infty))}\|f\|_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}2^{(\beta-n\delta_{2})(k-j)}2^{j(\lambda-\alpha(\infty))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\lambda q_{1}}\Big(\sum\limits_{j = -\infty}^{k}(k-j)^{m}2^{(j-k)(n\delta_{2}+\lambda-\beta-\alpha(\infty))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. \end{align*} |
The desired result is obtained by combining the estimates of J_{1} , J_{2} and J_{3} .
\mathbf{Theorem\; 3.2.} Let 0 < q_{1}\leq q_{2} < \infty , p_{2}(\cdot)\in\mathcal{P}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) and p_{1}(\cdot) be such that \frac{1}{p_{2}(\cdot)} = \frac{1}{p_{1}(\cdot)}-\frac{\beta}{n} . Also, let \omega^{p_{2}(\cdot)}\in A_{1} , b\in \mathrm{BMO}(\mathbb{R}^{n}) , \lambda > 0 and \alpha(\cdot)\in L^{\infty}(\mathbb{R}^{n})\cap \mathcal{C}^{\mathrm{log}}(\mathbb{R}^{n}) be log-Hölder continuous at the origin, with \lambda-n\delta_{1} < \alpha(0)\leq\alpha(\infty) , where \delta_{1}\in(0, 1) is the constant appearing in (2.12). Then,
\begin{align} \|\mathcal{H}^{\ast m}_{_{\beta,b}}f\|_{\mathrm{M\dot{K}}_{q_{2}, p_{2}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{2}(\cdot)})}\lesssim \|b\|^{m}_{\mathrm{BMO}}\|f\|_{\mathrm{M\dot{K}}_{q_{1}, p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. \end{align} | (3.16) |
Proof. From an application of the inequality of C_{p} , it is not difficult to see that
\begin{align*} |\mathcal{H}^{\ast m}_{_{\beta,b}}f(x)\chi_{k}(x)|&\leq \int_{\mathbb{R}^{n}\setminus B_{k}}|t|^{\beta-n}|b(x)-b(t)|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq \sum\limits_{j = k+1}^{\infty}\int_{A_{j}}|t|^{\beta-n}|b(x)-b(t)|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\lesssim \sum\limits_{j = k+1}^{\infty}\int_{A_{j}}|t|^{\beta-n}|b(x)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\; \; \; +\sum\limits_{j = k+1}^{\infty}\int_{A_{j}}|t|^{\beta-n}|b(t)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ & = F_{1}+F_{2}. \end{align*} | (3.17) |
For F_{1} , by the generalized Hölder inequality, we have
\begin{align*} F_{1}&\leq\sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\int_{A_{j}}|b(x)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq \sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}|b(x)-b_{A_{j}}|^{m}\cdot\chi_{k}(x)\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}. \end{align*} | (3.18) |
By taking the (L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})) -norm on both sides of (3.18) and using (2.15) of Lemma 2.9, we get
\begin{align*} \|F_{1}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))} &\leq \sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\||b(x)-b_{A_{j}}|^{m}\cdot\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\\ &\lesssim \sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}(j-k)^{m}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}. \end{align*} | (3.19) |
For F_{2} , by the generalized Hölder inequality, we have
\begin{align*} F_{2}&\leq\sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\int_{A_{j}}|b(t)-b_{A_{j}}|^{m}|f(t)|\mathrm{d}t\cdot\chi_{k}(x)\\ &\leq \sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\||b(t)-b_{A_{j}}|^{m}\cdot\chi_{j}(x)\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\cdot\chi_{k}(x). \end{align*} | (3.20) |
By taking the (L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})) -norm on both sides of (3.20) and using (2.15) of Lemma 2.9, we get
\begin{align*} \|F_{2}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))} &\leq \sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\||b(t)-b_{A_{j}}|^{m}\cdot\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\\ &\lesssim \sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}. \end{align*} | (3.21) |
Hence, from inequalities (3.17), (3.19) and (3.21), we get
\begin{align*} &\|\mathcal{H}^{\ast m}_{_{\beta,b}}f(x)\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\\ &\; \; \; \lesssim \|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\Big\{\sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}(j-k)^{m}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\\ &\; \; \; \; \; \; +\sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}\|b\|_{\mathrm{BMO}}^{m}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\Big\}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{m}\sum\limits_{j = k+1}^{\infty}2^{-j(n-\beta)}(j-k)^{m}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\|\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}. & \end{align*} | (3.22) |
On the other hand, by (2.7) and (2.8), combining (2.12) and (3.10), we have
\begin{align*} & 2^{-j(n-\beta)}\|\chi_{k}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})} \|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\\ &\; \; \; \; = 2^{j\beta}\|\chi_{k}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})} 2^{-jn}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\\ &\; \; \; \; \lesssim 2^{j\beta}\|\chi_{k}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})} \|\chi_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{-1}\\ &\; \; \; \; = 2^{j\beta} \|\chi_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{-1} \|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})} \frac{ \|\chi_{k}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}}{\|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}}\\ &\; \; \; \; \lesssim 2^{j\beta}2^{n\delta_{1}(k-j)} \|\chi_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{-1} \|\chi_{j}\|_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\; \; \; \; \lesssim 2^{j\beta}2^{n\delta_{1}(k-j)}2^{j(n-\beta)} \|\chi_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}^{-1} \|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}^{-1}\\ &\; \; \; \; = 2^{j\beta}2^{n\delta_{1}(k-j)}2^{-j\beta}\Big(2^{-jn}\|\chi_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\|\chi_{j}\|_{(L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)}))^{\prime}}\Big)^{-1} \lesssim 2^{n\delta_{1}(k-j)}. & \end{align*} | (3.23) |
Hence combining (3.22) and (3.23), we obtain
\begin{align*} \|\mathcal{H}^{\ast m}_{_{\beta,b}}f(x)\chi_{k}\|_{(L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)}))}\lesssim \|b\|_{\mathrm{BMO}}^{m}\sum\limits_{j = k+1}^{\infty}(j-k)^{m}2^{n\delta_{1}(k-j)}\|f_{j}\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}. & \end{align*} | (3.24) |
Next, by virtue of the condition q_{1}\leq q_{2} and the definition of weighted variable exponent Morrey-Herz space along with the use of Proposition 3.1, we get
\begin{align*} \|\mathcal{H}^{\ast m}_{_{\beta,b}}f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{2},p_{2}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{2}(\cdot)})}& = \sup\limits_{k_{0}\in \mathbb{Z}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(\cdot) q_{1}}\|\mathcal{H}^{\ast m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\leq \max\Big\{\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{\ast m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})},\\ &\; \; \; \; \; \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\Big(\sum\limits_{k = -\infty}^{-1}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{\ast m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\\ &\; \; \; \; \; \; \; \; \; +\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\|\mathcal{H}^{\ast m}_{\beta,b}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}\Big)\Big\}\\ & = \max\{Y_{1}, Y_{2}+Y_{3}\}, & \end{align*} | (3.25) |
where
\begin{align*} &Y_{1} = \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{\ast m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})},\\ &Y_{2} = \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{-1}2^{k\alpha(0) q_{1}}\|\mathcal{H}^{\ast m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})},\\ &Y_{3} = \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\|\mathcal{H}^{\ast m}_{_{\beta,b}}f\chi_{k}\|^{q_{1}}_{L^{p_{2}(\cdot)}(\omega^{p_{2}(\cdot)})}. \end{align*} |
First, we estimate Y_{1} . Since \lambda-n\delta_{1} < \alpha(0)\leq \alpha(\infty) , combining (3.24) and (3.13), we get
\begin{align*} Y_{1}&\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{n\delta_{1}(k-j)}\|f\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{n\delta_{1}(k-j)}2^{j(\lambda-\alpha(0))}\|f\|_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\alpha(0) q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}2^{n\delta_{1}(k-j)}2^{j(\lambda-\alpha(0))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0} < 0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = -\infty}^{k_{0}}2^{k\lambda q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}2^{(j-k)(\lambda-n\delta_{1}-\alpha(0))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. \end{align*} |
The estimate of Y_{2} is similar to that of Y_{1} .
Lastly, we estimate Y_{3} . Since \lambda-n\delta_{1} < \alpha(0)\leq \alpha(\infty) , combining (3.24) and (3.14), we get
\begin{align*} Y_{3}&\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{n\delta_{1}(k-j)}\|f\|_{L^{p_{1}(\cdot)}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}\|b\|_{\mathrm{BMO}}^{m}2^{n\delta_{1}(k-j)}2^{j(\lambda-\alpha(\infty))}\|f\|_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\alpha(\infty) q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}2^{n\delta_{1}(k-j)}2^{j(\lambda-\alpha(\infty))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}\sup\limits_{\substack{k_{0}\in\mathbb{Z}\\k_{0}\geq0}}2^{-k_{0}\lambda q_{1}}\sum\limits_{k = 0}^{k_{0}}2^{k\lambda q_{1}}\Big(\sum\limits_{j = k+1}^{\infty}(j-k)^{m}2^{(j-k)(\lambda-n\delta_{1}-\alpha(\infty))}\Big)^{q_{1}}\\ &\lesssim \|b\|_{\mathrm{BMO}}^{mq_{1}}\|f\|^{q_{1}}_{\mathrm{M\dot{K}}_{q_{1},p_{1}(\cdot)}^{\alpha(\cdot), \lambda}(\omega^{p_{1}(\cdot)})}. \end{align*} |
The desired result is obtained by combining the estimates of Y_{1} , Y_{2} and Y_{3} .
This paper considers the boundedness for m th order commutators of n- dimensional fractional Hardy operators \mathcal{H}^{m}_{_{\beta, b}} and adjoint operators \mathcal{H}_{\beta, b}^{\ast m} on weighted variable exponent Morrey-Herz spaces \mathrm{M\dot{K}}_{q, p(\cdot)}^{\alpha(\cdot), \lambda}(\omega) . When m = 0 , our main result holds on weighted variable exponent Morrey-Herz space for fractional Hardy operators and generalizes the result of Asim et al. in [1, Theorems 4.2 and 4.3]. When m = 1 , our main result holds on weighted variable exponent Morrey-Herz space for commutators of the fractional Hardy operators and generalizes the result of Hussain et al. in [12, Theorems 18 and 19].
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
This paper is supported by the National Natural Science Foundation of China (Grant No. 12161071), Qinghai Minzu University campus level project (Nos. 23GH29, 23GCC10).
All authors declare that they have no conflicts of interest.
[1] |
M. A. A. Mousa, A. T. Elgohr, H. A. Khater, Trajectory optimization for a 6 DOF robotic arm based on reachability time, Annals of Emerging Technologies in Computing, 8 (2024), 22–35. https://doi.org/10.33166/AETiC.2024.01.003 doi: 10.33166/AETiC.2024.01.003
![]() |
[2] |
A. Krisbudiman, T. H. Nugroho, A. Musthofa, Analysis industrial robot arm with Matlab and RoboAnalyzer, International Journal of Advanced Engineering, Management and Science, 7 (2021), 75–80. https://doi.org/10.22161/ijaems.73.10 doi: 10.22161/ijaems.73.10
![]() |
[3] | J. W. Lee, G. T. Park, J. S. Shin, J. W. Woo,, Industrial robot calibration method using denavit-Hatenberg parameters, 2017 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), 2017, 1834–1837. https://doi.org/10.23919/ICCAS.2017.8204265 |
[4] | Z. Y. He, J. C. Li, Six-degree-of-freedom robot trajectory planning based on MATLAB, International Conference on Automation, Robotics and Computer Engineering (ICARCE), Wuhan, China, 2022, 1–3. https://doi.org/10.1109/ICARCE55724.2022.10046483 |
[5] | KR 22 R1610-2-KUKA AG. Available from: https://www.infinitysolutions.co.jp/wprenew/wp-content/uploads/2021/02/kr_cybertech_en.pdf. |
[6] | D. Constantin, M. Lupoae, C. Baciu, B. D. Ilie, Forward kinematic analysis of an industrial robot, International Conference on Mechanical Engineering (ME 2015), Vienna, Austria 2015, 90–95. |
[7] |
W. Chen, X. Li, H. L. Ge, L. Wang, Y. H. Zhang, Trajectory planning for spray painting robot based on point cloud slicing technique, Electronics, 9 (2020), 908. https://doi.org/10.3390/electronics9060908 doi: 10.3390/electronics9060908
![]() |
[8] | T. P. Singh, P. Suresh, S. Chandan, Forward and inverse kinematic analysis of robotic manipulators, International Research Journal of Engineering and Technology, 4 (2017), 1459–1469. |
[9] |
J. Villalobos, I. Y. Sanchez, F. Martell, Singularity analysis and complete methods to compute the inverse kinematics for a 6-DOF UR/TM-type robot, Robotics, 11 (2022), 137. https://doi.org/10.3390/robotics11060137 doi: 10.3390/robotics11060137
![]() |
[10] |
D. Sivasamy, M. D. Anand, K. A. Sheela, Robot forward and inverse kinematics research using MATLAB, International Journal of Recent Technology and Engineering, 8 (2019), 29–35 https://doi.org/10.35940/ijrte.b1006.0782s319 doi: 10.35940/ijrte.b1006.0782s319
![]() |
[11] |
A. Patwardhan, A. Prakash, R. G. Chittawadigi, Kinematic analysis and development of simulation software for nex dexter robotic manipulator, Procedia Computer Science, 133 (2018), 660–667. https://doi.org/10.1016/j.procs.2018.07.101 doi: 10.1016/j.procs.2018.07.101
![]() |
[12] | M. Kaur, S. Sondhi, V. K. Yanumula, Kinematics analysis and jacobian calculation for six degrees of freedom robotic arm, 2020 IEEE 17th India Council International Conference (INDICON), New Delhi, India, 2020, 1–6. https://doi.org/10.1109/INDICON49873.2020.9342093 |
[13] | S. KuCuk, Z. Bingul, The inverse kinematics solutions of industrial robot manipulators, Proceedings of the IEEE International Conference on Mechatronics, Istanbul, Turkey, 2004,274–279. https://doi.org/10.1109/ICMECH.2004.1364451 |
[14] | M. G. Krishnan, S. Ashok, Kinematic analysis and validation of an industrial robot manipulator, 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, 1393–1399. https://doi.org/10.1109/TENCON.2019.8929712 |
[15] |
D. P. Nayak, K. C Rath, Robot kinematics analysis with simulation of manipulator trajectory utilising the DH parameter, YMER, 21 (2022), 273–285. https://doi.org/10.37896/ymer21.08%2F24 doi: 10.37896/ymer21.08%2F24
![]() |
[16] |
A. El-Sherbiny, M. A. Elhosseini, A. Y Haikal, A comparative study of soft computing methods to solve inverse kinematics problem, Ain Shams Eng. J., 9 (2018), 2535–2548. https://doi.org/10.1016/j.asej.2017.08.001 doi: 10.1016/j.asej.2017.08.001
![]() |
[17] |
I. Chavdarov, B. Naydenov, Algorithm for determining the types of inverse kinematics solutions for sequential planar robots and their representation in the configuration space, Algorithms, 15 (2022), 469. https://doi.org/10.3390/a15120469 doi: 10.3390/a15120469
![]() |
[18] |
S. S. Chauhan, A. K. Khare, Kinematic analysis of the ABB IRB 1520 industrial robot using RoboAnalyzer software, Evergreen, 7 (2022), 510–518. https://doi.org/10.5109/4150470 doi: 10.5109/4150470
![]() |
[19] | M. W. Spong, S. Hutchinson, M. Vidyasagar, Robot modeling and control, 2 Eds., Hoboken: Wiley, 2020. https://doi.org/10.1109/MCS.2006.252815 |
[20] | B. Siciliano, O. Khatib, Robotics and the handbook, In: Springer handbook of robotics, Cham: Springer, 2016, 1–6. https://doi.org/10.1007/978-3-319-32552-1 |
[21] | Z. Bingul, H. M. Ertunc, C. Oysu, Comparison of inverse kinematics solutions using neural network for 6R robot manipulator with offset, 2005 ICSC Congress on Computational Intelligence Methods and Applications, Istanbul, Turkey, 2005, 5. https://doi.org/10.1109/CIMA.2005.1662342 |
[22] | P. Corke, Robotics and control: Fundamental algorithms in MATLAB, Cham: Springer, 2022. https://doi.org/10.1007/978-3-030-79179-7 |
[23] | I. Mehta, K. Bimbraw, R. G. Chittawadigi, S. K. Saha, A teach pendant to control virtual robots in Roboanalyzer, 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), Amritapuri, India, 2016, 1–6. https://doi.org/10.1109/RAHA.2016.7931881 |
[24] |
P. I. Corke, A robotics toolbox for MATLAB, IEEE Robot. Autom. Mag., 3 (1996), 24–32. https://doi.org/10.1109/100.486658 doi: 10.1109/100.486658
![]() |
[25] |
A. El-Sherbiny, M. A. Elhosseini, A. Y. Haikal, A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm, Appl. Soft Comput., 73 (2018), 24–38. https://doi.org/10.1016/j.asoc.2018.08.028 doi: 10.1016/j.asoc.2018.08.028
![]() |
[26] |
M. A. A. Mousa, A. T. Elgohr, H. A.Khater, Path planning for a 6 DoF robotic arm based on whale optimization algorithm and genetic algorithm, J. Eng. Res., 7 (2023), 160–168. https://doi.org/10.21608/erjeng.2023.237586.1256 doi: 10.21608/erjeng.2023.237586.1256
![]() |
[27] |
H. Danaci, L. A. Nguyen, T. L. Harman, M. Pagan, Inverse kinematics for serial robot manipulators by particle swarm optimization and POSIX threads implementation, Appl. Sci., 13 (2023), 4515. https://doi.org/10.3390/app13074515 doi: 10.3390/app13074515
![]() |
[28] |
S. Djeffal, C. Mahfoudi, Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches, SIMULATION, 99 (2023), 817–830. https://doi.org/10.1177/00375497231164645 doi: 10.1177/00375497231164645
![]() |
[29] | R. Sadanand, R. G. Chittawadigi, R. P. Joshi, S. K Saha, Virtual robots module: An effective visualization tool for robotics toolbox, Proceedings of the 2015 Conference on Advances In Robotics, Goa, India, 2015, 1–6. https://doi.org/10.1145/2783449.2783475 |
[30] | A. N. Barakat, K. A. Gouda, K. A Bozed, Kinematics analysis and simulation of a robotic arm using MATLAB., 2016 4th International Conference on Control Engineering & Information Technology (CEIT), Hammamet, Tunisia, 2016, 1–5. https://doi.org/10.1109/CEIT.2016.7929032 |
[31] | Y. L. Bao, K. M. Hamza, K. D. Kallu, S. J. Abbasi, M. C. Lee, A study on 7-DOF manipulator control by using MATLAB robotics toolbox, 2019 16th International Conference on Ubiquitous Robots, Jeju, Korea, 2019, 24–27. |
[32] |
D. T. Long, T. V. Binh, R. V. Hoa, L. V. Anh, N. V. Toan, Robotic arm simulation by using matlab and robotics toolbox for industry application, International Journal of Electronics and Communication Engineering, 7 (2020), 1–4. https://doi.org/10.14445/23488549%2Fijece-v7i10p101 doi: 10.14445/23488549%2Fijece-v7i10p101
![]() |
[33] |
D. Q. Zhang, Z. Y. Peng, G. S. Ning, X. Han, Positioning accuracy reliability of industrial robots through probability and evidence theories, J. Mech. Des., 143 (2021), 011704. https://doi.org/10.1115/1.4047436 doi: 10.1115/1.4047436
![]() |
[34] |
D. Q. Zhang, X. Han, Kinematic reliability analysis of robotic manipulator, J. Mech. Des., 142 (2020), 044502. https://doi.org/10.1115/1.4044436 doi: 10.1115/1.4044436
![]() |
[35] |
D. Q. Zhang, S. S. Shen, J. H. Wu, F. Wang, X. Han, Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors, Reliab. Eng. Syst. Safe., 229 (2023), 108808. https://doi.org/10.1016/j.ress.2022.108808 doi: 10.1016/j.ress.2022.108808
![]() |
[36] | J. Bahuguna, R. G. Chittawadigi, S. K. Saha, Teaching and learning of robot kinematics using RoboAnalyzer software, In: Proceedings of conference on advances in robotics, New York: Association for Computing Machinery, 2013, 1–6. https://doi.org/10.1145/2506095.2506142 |
[37] | V. Gupta, R. G. Chittawadigi, S. K. Saha, RoboAnalyzer: Robot visualization software for robot technicians, In: Proceedings of the advances in robotics, Association for Computing Machinery, 2017, 1–5. https://doi.org/10.1145/3132446.3134890 |
[38] |
R. S. Othayoth, R. G. Chittawadigi, R. P. Joshi, S. K. Saha, Robot kinematics made easy using RoboAnalyzer software, Comput. Appl. Eng. Educ., 25 (2017), 669–680. https://doi.org/10.1002/cae.21828 doi: 10.1002/cae.21828
![]() |
[39] | P. Chang, A closed-form solution for the control of manipulators with kinematic redundancy, 1986 IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, 1986, 9–14. https://doi.org/10.1109/ROBOT.1986.1087725 |
[40] |
P. Chang, A closed-form solution for inverse kinematics of robot manipulators with redundancy, IEEE Journal on Robotics and Automation, 3 (1987), 393–403. https://doi.org/10.1109/jra.1987.1087114 doi: 10.1109/jra.1987.1087114
![]() |
[41] | I. M. Chen, Y. Gao, Closed-form inverse kinematics solver for reconfigurable robots, IEEE International Conference on Robotics and Automation, Seoul, South Korea, 2001, 2395–2400. https://doi.org/10.1109/ROBOT.2001.932980 |
[42] |
J. Gao, B. Zhou, B. Zi, S. Qian, P. Zhao, Kinematic uncertainty analysis of a Cable-Driven parallel robot based on an error transfer model, J. Mechanisms Robotics, 14 (2022), 051008. https://doi.org/10.1115/1.4053219 doi: 10.1115/1.4053219
![]() |
[43] |
D. Q. Zhang, Z. H. Han, F. Wang, X. Han, Proficiency of statistical moment-based methods for analysis of positional accuracy reliability of industrial robots, Int. J. Mech. Mater. Des., 17 (2021), 403–418. https://doi.org/10.1007/s10999-021-09532-2 doi: 10.1007/s10999-021-09532-2
![]() |
[44] |
Q. Q. Zhao, J. K. Guo, D. T. Zhao, D. W. Yu, J. Hong, Time-dependent system kinematic reliability analysis for robotic manipulators, J. Mech. Des., 143 (2021), 041704. https://doi.org/10.1115/1.4049082 doi: 10.1115/1.4049082
![]() |
[45] |
J. A. Abdor-Sierra, E. A. Merchán-Cruz, R. G. Rodríguez-Cañizo, A comparative analysis of metaheuristic algorithms for solving the inverse kinematics of robot manipulators, Results in Engineering, 16 (2022), 100597. https://doi.org/10.1016/j.rineng.2022.100597 doi: 10.1016/j.rineng.2022.100597
![]() |
[46] | C. J. Liu, X. Y. Wang, H. S. Jiang, X. Y. Wang, H. Y. Guo, Inverse kinematics solution of manipulator based on IPSO-BPNN, 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Chengdu, China, 2022,175–179. https://doi.org/10.1109/PRAI55851.2022.9904288 |
[47] |
A. X. Wu, Z. P. Shi, Y. D. Li, M. H. Wu, Y. Guan, J. Zhang, et al., Formal kinematic analysis of a general 6R manipulator using the screw theory, Math. Probl. Eng., 2015 (2015), 549797. https://doi.org/10.1155/2015/549797 doi: 10.1155/2015/549797
![]() |
[48] |
Q. D. Li, H. H. Ju, P. F. Xiao, Kinematics analysis and optimization of 6R manipulator, IOP Conf. Ser.: Mater. Sci. Eng., 816 (2020), 012016. https://doi.org/10.1088/1757-899X/816/1/012016 doi: 10.1088/1757-899X/816/1/012016
![]() |
[49] | M. T. Nguyen, C. Yuan, J. H. Huang, Kinematic analysis of a 6-DOF robotic arm, In: Mechanisms and machine science, Cham: Springer, 2019, 2965–2974. https://doi.org/10.1007/978-3-030-20131-9_292 |
[50] |
H. A. R. Akkar, A. N. A-Amir, Kinematics analysis and modeling of 6 degree of freedom robotic arm from DFROBOT on Labview, Research Journal of Applied Sciences, Engineering and Technology, 7 (2016), 569–575. https://doi.org/10.19026/rjaset.13.3016 doi: 10.19026/rjaset.13.3016
![]() |
[51] | A. Talli, A. C. Giriyapur, Kinematic analysis and simulation of industrial robot based on RoboAnalyzer, In: Recent advances in mechanical infrastructure, Singapore: Springer, 2021,473–483. https://doi.org/10.1007/978-981-33-4176-0_40 |
[52] |
J. Z. Vidaković, M. P. Lazarević, V. M. Kvrgić, Z. Z. Dančuo, G. Z. Ferenc, Advanced quaternion forward kinematics algorithm including overview of different methods for robot kinematics, FME Trans., 42 (2014), 189–199. https://doi.org/10.5937/fmet1403189v doi: 10.5937/fmet1403189v
![]() |
[53] |
T. Aravinthkumar, M. Suresh, B. Vinod, Kinematic analysis of 6 DOF articulated robotic arm, International Research Journal of Multidisciplinary Technovation, 3 (2021), 1–5. https://doi.org/10.34256/irjmt2111 doi: 10.34256/irjmt2111
![]() |
[54] |
K. S. Gaeid, A. F. Nashee, I. A. Ahmed, M. H. Dekheel, Robot control and kinematic analysis with 6DoF manipulator using direct kinematic method, Bulletin of Electrical Engineering and Informatics, 10 (2021), 70–78. https://doi.org/10.11591/eei.v10i1.2482 doi: 10.11591/eei.v10i1.2482
![]() |
[55] | M. Dahari, J. D. Tan, Forward and inverse kinematics model for robotic welding process using KR-16KS KUKA robot, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization, Kuala Lumpur, Malaysia, 2011, 1–6. https://doi.org/10.1109/ICMSAO.2011.5775598 |
[56] | J. X. Yu, D. Z. You, J. S. Liu, Analysis of inverse kinematics method for six degrees of freedom manipulator based on MATLAB, 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE), Beijing, China, 2017,211–215. https://doi.org/10.1109/CCSSE.2017.8087925 |
[57] |
S. Asif, P. Webb, Kinematics analysis of 6-DoF articulated robot with spherical wrist, Math. Probl. Eng., 2021 (2021), 6647035. https://doi.org/10.1155/2021/6647035 doi: 10.1155/2021/6647035
![]() |
[58] |
P. Corke, MATLAB toolboxes: Robotics and vision for students and teachers, IEEE Robot. Autom. Mag., 14 (2007), 16–17. https://doi.org/10.1109/m-ra.2007.912004 doi: 10.1109/m-ra.2007.912004
![]() |
[59] | E. Drumwright, J. Hsu, N. Koenig, D. Shell, Extending open dynamics engine for robotics simulation, In: Simulation, modeling, and programming for autonomous robots, Berlin: Springer, 2010, 38–50. https://doi.org/10.1007/978-3-642-17319-6_7 |
[60] |
N. A. S. Laksana, R. Ariawan, U. S. Jati, J. Sodikin, Ulikaryani, Analisis kinematik singularty pada manipulator 7 DOF dengan software simulasi RoboAnalyzer, Infotekmesin, 13 (2022), 265–271. https://doi.org/10.35970/infotekmesin.v13i2.1538 doi: 10.35970/infotekmesin.v13i2.1538
![]() |
[61] |
J. F. Nethery, M. W.Spong, Robotica: A mathematica package for robot analysis, IEEE Robot. Autom. Mag., 1 (1994), 13–20. https://doi.org/10.1109/100.296449 doi: 10.1109/100.296449
![]() |
[62] |
M. F. Robinette, R. Manseur, Robot-draw, an internet-based visualization tool for robotics education, IEEE T. Educ., 44 (2001), 29–34. https://doi.org/10.1109/13.912707 doi: 10.1109/13.912707
![]() |
[63] |
M. Morozov, S. G. Pierce, C. N. MacLeod, C. Mineo, R. Summan, Off-line scan path planning for robotic NDT, Measurement, 122 (2018), 284–290. https://doi.org/10.1016/j.measurement.2018.02.020 doi: 10.1016/j.measurement.2018.02.020
![]() |
[64] | A. Garbev, A. Atanassov, Comparative analysis of RoboDK and robot operating system for solving diagnostics tasks in off-line programming, 2020 International Conference Automatics and Informatics (ICAI), Varna, Bulgaria, 2020, 1–5. https://doi.org/10.1109/ICAI50593.2020.9311332 |
[65] |
M. K. Elshaarawy, A. K. Hamed, Predicting discharge coefficient of triangular side orifice using ANN and GEP models, Water Science, 38 (2024), 1–20. https://doi.org/10.1080/23570008.2023.2290301 doi: 10.1080/23570008.2023.2290301
![]() |
[66] |
U. Khair, H. Fahmi, S. A. Hakim, R. Rahim, Forecasting error calculation with mean absolute deviation and mean absolute percentage error, J. Phys.: Conf. Ser., 930 (2017), 012002. https://doi.org/10.1088/1742-6596/930/1/012002 doi: 10.1088/1742-6596/930/1/012002
![]() |
1. | Rishi Kumar Pandey, Kottakkaran Sooppy Nisar, Enhanced numerical techniques for solving generalized rotavirus mathematical model via iterative method and ρ-Laplace transform, 2024, 12, 26668181, 100963, 10.1016/j.padiff.2024.100963 | |
2. | Huda Alsaud, Muhammad Owais Kulachi, Aqeel Ahmad, Mustafa Inc, Muhammad Taimoor, Investigation of SEIR model with vaccinated effects using sustainable fractional approach for low immune individuals, 2024, 9, 2473-6988, 10208, 10.3934/math.2024499 | |
3. | Cicik Alfiniyah, Tutik Utami, Nashrul Millah, Reuben Iortyer Gweryina, Optimal control and stability analysis of an alcoholism model with treatment centers, 2025, 22150161, 103311, 10.1016/j.mex.2025.103311 |