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Research article Special Issues

Adjusting non-pharmaceutical interventions based on hospital bed capacity using a multi-operator differential evolution

  • Without vaccines and medicine, non-pharmaceutical interventions (NPIs) such as social distancing, have been the main strategy in controlling the spread of COVID-19. Strict social distancing policies may lead to heavy economic losses, while relaxed social distancing policies can threaten public health systems. We formulate optimization problems that minimize the stringency of NPIs during the prevaccination and vaccination phases and guarantee that cases requiring hospitalization will not exceed the number of available hospital beds. The approach utilizes an SEIQR model that separates mild from severe cases and includes a parameter μ that quantifies NPIs. Payoff constraints ensure that daily cases are decreasing at the end of the prevaccination phase and cases are minimal at the end of the vaccination phase. Using a penalty method, the constrained minimization is transformed into a non-convex, multi-modal unconstrained optimization problem. We solve this problem using the improved multi-operator differential evolution, which fared well when compared with other optimization algorithms. We apply the framework to determine optimal social distancing strategies in the Republic of Korea given different amounts and types of antiviral drugs. The model considers variants, booster shots, and waning of immunity. The optimal μ values show that fast administration of vaccines is as important as using highly effective vaccines. The initial number of infections and daily imported cases should be kept minimum especially if the bed capacity is low. In Korea, a gradual easing of NPIs without exceeding the bed capacity is possible if there are at least seven million antiviral drugs and the effectiveness of the drug in reducing severity is at least 86%. Model parameters can be adapted to a specific region or country, or other infectious diseases. The framework can be used as a decision support tool in planning economic policies, especially in countries with limited healthcare resources.

    Citation: Victoria May P. Mendoza, Renier Mendoza, Jongmin Lee, Eunok Jung. Adjusting non-pharmaceutical interventions based on hospital bed capacity using a multi-operator differential evolution[J]. AIMS Mathematics, 2022, 7(11): 19922-19953. doi: 10.3934/math.20221091

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  • Without vaccines and medicine, non-pharmaceutical interventions (NPIs) such as social distancing, have been the main strategy in controlling the spread of COVID-19. Strict social distancing policies may lead to heavy economic losses, while relaxed social distancing policies can threaten public health systems. We formulate optimization problems that minimize the stringency of NPIs during the prevaccination and vaccination phases and guarantee that cases requiring hospitalization will not exceed the number of available hospital beds. The approach utilizes an SEIQR model that separates mild from severe cases and includes a parameter μ that quantifies NPIs. Payoff constraints ensure that daily cases are decreasing at the end of the prevaccination phase and cases are minimal at the end of the vaccination phase. Using a penalty method, the constrained minimization is transformed into a non-convex, multi-modal unconstrained optimization problem. We solve this problem using the improved multi-operator differential evolution, which fared well when compared with other optimization algorithms. We apply the framework to determine optimal social distancing strategies in the Republic of Korea given different amounts and types of antiviral drugs. The model considers variants, booster shots, and waning of immunity. The optimal μ values show that fast administration of vaccines is as important as using highly effective vaccines. The initial number of infections and daily imported cases should be kept minimum especially if the bed capacity is low. In Korea, a gradual easing of NPIs without exceeding the bed capacity is possible if there are at least seven million antiviral drugs and the effectiveness of the drug in reducing severity is at least 86%. Model parameters can be adapted to a specific region or country, or other infectious diseases. The framework can be used as a decision support tool in planning economic policies, especially in countries with limited healthcare resources.



    n,nu,nw,ny are the number of states, inputs, input noise, and outputs, respectively.

    q is the dimension of the nonlinearity (qn).

    uRnu is the vector of known inputs.

    ˜ueq is the equivalent output of the nonlinearity function.

    vRny is a vector of unknown measurement noise.

    wRnw is a vector of unknown input disturbance.

    wext=col(w(t),x(t),ϕ(t,u,x),u(t)) are the extended errors.

    xRn is the state vector.

    ˆxRn is the state estimate vector.

    ˜x=xˆx is the state estimation error vector of the system.

    xd is the state of delayed measurement.

    ˆxa is the approximation of ˆx (when delay is approximated).

    ˜xa is the approximation of ˜x (when delay is approximated).

    yRny is the vector of measurement.

    ˜yeq is the equivalent input to the nonlinearity.

    yˉδ is the effect of error in modeling the delay.

    zeq is the state of the augmented equivalent system.

    x,y,u are the states, output, and input of system used in the proof of bounded real lemma.

    A,ΔARn×n are the nominal system matrix and its deviation, respectively, with Asys=A+ΔA.

    AΓ is a delay system matrix.

    Ad,Bd, and Cd are the system matrices of delay.

    ˉA,ˉC,ˉL,ˉE,ˉFext,ˉG are system matrices in extended state space.

    E,ΔERn×qare the nominal nonlinearity gain matrix and its deviation, respectively, with Esys=E+ΔE.

    FRn×nw is the gain matrix for input disturbance.

    Fext=[F  ΔA  ΔE  ΔH] is the extended error-gain matrix.

    G is the matrix gain used in matrix Lipschitz condition.

    H,ΔHRn×nuare the nominal input-gain matrix and its deviation, respectively, with Hsys=H+ΔH.

    L is the observer gain.

    K is the degree of rational approximation of delay.

    ˉP is a Lyapunov matrix in the extended state-space.

    QRny×ny,RextRnwext×nwext, and WRn×n are weighting matrices.

    T is the time period of the moving time-averaged Lyapunov function.

    V1:Rn1R and V2:Rn2R, V:R×RnR are arbitrary functions.

    V(t,x) is an average energy function.

    VI(τ,x) is an energy function (being averaged).

    A,B,C,D are arbitrary matrices.

    G(s) is the transfer function from the output to the input of the nonlinearity.

    Gd(s) is the transfer function of delay.

    Gd(s) is the approximate rational transfer function of delay.

    U,V,W,Z are arbitrary matrices used in the matrix inverse lemma.

    V is an arbitrary energy function used in the proof of bounded real lemma.

    Vt:=V(t,x)/t,Vx:=V(t,x)/2x are partial derivatives of V.

    T is the inverse-projection matrix with TT=[ˉ0TKny×nIn×n]T.

    γ is H norm of the system used in the proof of bounded real lemma.

    γC is the effective bound on the error dynamics.

    ε,ϵϕ is an arbitrary constant.

    δGd is the approximation error transfer function.

    ˉδGd is an equivalent approximation error transfer function.

    ςk,ˉςk are Taylor coefficients.

    ϕ=ϕ(t,u,x):R×Rm×RnRq is the vector nonlinearity.

    ˜ϕ=˜ϕ(t,u,ˆx,˜x)=ϕ(t,u,x)ϕ(t,u,ˆx) is the error in the nonlinearity.

    ω is the frequency.

    ωmax is the frequency where gain of GNL is maximum.

    ωδ is the frequency at which the gain of δGdis maximum.

    Γ is the time delay.

    Θ(ω) is the matrix frequency gain function of the observer error dynamics.

    ˉΘ(ω) is the matrix frequency gain function of the observer error dynamics with equivalent delay.

    Ξ,Ω are arbitrary matrices in extended state space.

    All physical systems exhibit varying levels of nonlinearities. While some nonlinearities may be negligible, it is often necessary to implement a nonlinear controller to ensure stability and system performance [1]. Many advanced control techniques, such as backstepping and sliding mode control, use all system states [1]. Since not all system states are directly measured, it is often necessary to utilize a nonlinear observer to estimate unmeasured states. Physical systems also experience at least a small amount of delay between sensing and activation due to physical limits on processing and communication speed. Non-trivial amounts of delay are known to destabilize systems [2] and observers [3,4,5]. Most observer designs tend to neglect modeling uncertainties, unknown inputs, and measurement noises [6,7,8,9,10]. Hence, this paper focuses on developing observers for systems with measurement delays.

    Lipschitz [11,12,13,14,15] and matrix Lipschitz [16,17] nonlinearities are the most common classes of nonlinearities considered in the literature. Here, the observer design problem is formulated as a solution to a linear matrix inequality (LMI). LMI-based formulation is particularly attractive owing to the availability of fast commercial solvers. Such LMI-based designs have been extended to delay-free Lipschitz and matrix Lipschitz nonlinear systems with input disturbances and measurement noise [6,7,8], as well as to perform sensor fault detection [18], actuator fault detection [19], and unknown parameter estimation [9,10]. Other literature has utilized high-gain sliding mode observers [20,21,22] to improve the robustness of systems without measurement noises or measurement delays. More recent literature has used a new time-averaged Lyapunov function to design sliding mode nonlinear observers for systems with sensor noise [23,24]. High-gain observers have also been used for extended state observers for uncertain nonlinear systems without sensor noise or measurement delay [25]. Fault reconstruction algorithms have also been extended to noise-free one-sided Lipchitz nonlinear descriptor systems [26]. However, these results require systems with special structures and cannot be extended to general systems.

    The most common method for analyzing the stability of a system with delay is through the use of Lyapunov–Krasowskii (LK) or Lyapunov–Razumikhin (LR) functionals. LK and LR are constructed by adding a quadratic integral term to the traditional quadratic Lyapunov function. While there is ample literature on control [27,28,29,30] and parameter estimation [31,32,33] in systems with non-measurement delay (i.e., delay only in the state dynamics with, e.g., no measurement delay), this review instead focuses on the literature on measurement delay. Zhou et al. [34] proposed an observer for a stochastic linear system to implement non-fragile observer-based H control. Kazantzis and Wright [35] used a linearizing transform (potentially through feedback linearization) on a noise-free nonlinear system. These works do not, however, provide an explicit method for calculating the observer gain. Cacace et al. [36] presented a state observer for drift observable nonlinear systems where the output measurements are affected by a known and bounded time-varying delay. Majeed et al. [3] developed a nonlinear observer-based control of a noise-free Lipschitz nonlinear system whose stability is demonstrated using an LK functional. He and Liu [4,5] designed a noise-free nonlinear observer for a system in feedback linearization. However, these works do not provide an explicit design procedure. Vafaei and Yazdanpanah [37] proposed a "chain observer" for a Lipschitz nonlinear system; again, the overall stability was demonstrated using the LF functional. Chakrabarty et al. [38] presented an LMI-based sufficient condition for the design of state and unknown input observers, which as in earlier results, requires the underlying system dynamics to be stable. Huong [39] proposed an observer design for a noise-free linear system with sensor delay without utilizing LK/LF functionals. However, the observer design relies on the existence of an esoteric state transformation that would be difficult to extend to nonlinear systems with noise.

    More recently, Targui et al. [40] designed an observer for a noise-free nonlinear system adding a state-integral term. The state-integral term aims to project the sensor measurement to the current time. However, the need for an additional integration term would make the observer design impractical, and the conditions for the observer design are conservative. Furthermore, satisfying the provided condition does not appear to guarantee observer stability (a more detailed explanation is provided in the Appendix). Guarro et al. [41] proposed a hybrid observer for a linear system, which uses a similar state-integral term to project sensor measurements to current time. There have been other variations of LK/LF functionals for specific systems such as wind turbines [42] or using neural networks [43] for noise-free systems that also require inherent system stability.

    It should be noted that all LMI-based formulations require us to find an observer gain that would make the LMI negative. The LK- and LR-based formulation used in previous literature typically result in a positive definite term being added to the LMI. In the case of observers, this implies that previous literature can only design stable observers when the underlying system is stable. Further, LK- and LR-based formulation would require the delay to be smaller than the time scale of the error dynamics [44]. Additionally, most previous results have focused on linear or nonlinear systems without any noise. Hence, this paper aims to develop an observer design that can be applied to any magnitude of delay. The observer design would make the nominal observer stable or would guarantee a L2 performance in the presence of modeling uncertainties, sensor noise, and input disturbance. The key contributions of this paper lie in developing a new observer design procedure that

    ● can be implemented on a very wide class of nonlinear systems in the presence of measurement noise and disturbances,

    ● works for large measurement delays,

    ● does not require stability of the underlying system,

    ● is robust to modeling uncertainties,

    ● can guarantee stability in the absence of noise and L2 performance in the presence of modeling uncertainties, sensor noise, and input disturbance,

    ● provides both necessary and sufficient conditions for the existence of the observer.

    Additionally, the implementation of the algorithm has been demonstrated using multiple illustrative examples. The methodology for calculating the explicit solution can be extended to intermittent measurements. A previous observer design by Targui et al. [40] was shown to produce an unstable observer for certain systems.

    Consider a nonlinear system with measurement delay given by

    ˙x(t)=Asysx(t)+Esysϕ(t,u,x)+Hsysu(t)+Fw(t), (2.1)
    y(t)=Cx(tΓ)+v(t), (2.2)
    Asys=A+ΔA,Esys=E+ΔE,Hsys=H+ΔH, (2.3)

    v,w can be zero-mean white noises. The C-matrix is assumed to not deviate from its nominal value as any error in the C-matrix can be incorporated in ΔA,ΔE, and ΔH. The nonlinearity ϕ=ϕ(t,u,x):R×Rm×RnRq is assumed to satisfy a matrix Lipschitz condition

    |ϕ(t,u,x)ϕ(t,u,ˆx)||G(x(t)ˆx(t))|,GRq×n. (2.4)

    The matrix Lipschitz condition is a fairly general condition as all nonlinear systems would become matrix Lipschitz in a small neighborhood around their normal operating point. (Note: we may assume σmax(G)=1 or σmax(E)=1). We can construct an observer using the nominal system as follows:

    ˙ˆx(t)=Aˆx(t)+Eϕ(t,u,ˆx)+L(y(t)Cˆx(tΓ))+Hu(t), (2.5)

    The estimation error dynamics can be written as

    ˙˜x(t)=A˜x(t)+Fextwext(t)LC˜x(tΓ)Lv(t)+E˜ϕ(t,u,ˆx,˜x). (2.6)

    We will now determine observer gains that can drive ˜x to an invariant subspace when w0,v0 and [ΔA  ΔE  ΔH]0, or drive ˜x0 when w=0,v=0 and [ΔA  ΔE  ΔH]=0. To this end, we will define preliminary lemmas in section 2.2 that will help us formulate LMIs. Next, we will create a state-space approximation of the delay and define a bound on the approximation error in section 2.3.

    Lemma 1: S-Procedure Lemma [45]: If V1:Rn1R and V2:Rn2R be such that V20, then V1<0 iff ε>0 such that

    V1εV2<0. (2.7)

    Proof: If V1<0, then we can choose εmin(V1)max(V2) to obtain Eq (2.7). Now, if Eq (2.7) is valid, since ε>0, and V20, εV20. Hence, we find that 0>V1εV2V1.

    Lemma 2: Time-averaged Lyapunov function [23,24] or, equivalently, the moving average function: If there exists a moving average function

    V(t,x)=1TttTVI(τ,x)dτ, (2.8)

    s.t., VI(τ,x)>0 xˉ0, 1TttT˙VI(τ,x)dτ<0, when 1TttTxTWxdτ>D then the system achieves a L2 performance 1TttTxTWxdτD.

    Proof: Note that

    V(t+dt)=1Tt+dtt+dtTVI(τ)dt=ttTVI(τ+dt,x(τ+dt))dτ, (2.9)

    hence

    ˙V(t)=1TttT˙VI(τ)dτ. (2.10)

    Using LaSalle's invariance principle [46], we can show that the system converges to a subspace where ˙V=0, which corresponds to 1TttTxTWxdτD.

    Lemma 3: Matrix inverse lemma or Woodbury lemma [47]:

    [ABCD]1=[A1+A1B(DCA1B)1CA1A1B(DCA1B)1(DCA1B)1CA1(DCA1B)1]. (2.11)

    Lemma 4: Bounded real lemma extended to a system with delay: Consider a system with a transfer function

    G(s)=G[sI(A+AΓGd(s))]1E, (2.12)

    and an equivalent state space representation

    ˙x(t)=Ax(t)+AΓx(tΓ)+Eu(t),y(t)=Gx(t). (2.13)

    Let V=V(t,x):R×RnR be an energy function satisfying

    V(t,x)>0 xˉ0,V(t,x)=0ifx=ˉ0, (2.14)
    Vt:=V(t,x)/t,Vx:=V(t,x)/2x. (2.15)

    For this system, the following statements are equivalent:

    ⅰ. The H norm of the transfer function is bounded by γ

    ||G(s)||<γ. (2.16)

    ⅱ. V satisfying Eq (2.14) and ϵϕR0 s.t., x,

    Vt+Vx[Ax(t)+AΓx(tΓ)]+[Ax(t)+AΓx(tΓ)]TVTx                +ϵϕγ2xT(t)GTGx(t)+ϵ1ϕVxEETVTx<0. (2.17)

    ⅲ. V satisfying Eq (2.14) and ϵϕR0 s.t. x,u

    Vt+Vx[Ax(t)+AΓx(tΓ)+Eu(t)]+[Ax(t)+AΓx(tΓ)+Eu(t)]TVTx+ϵϕx(t)GTGx(t)ϵϕγ2uT(t)u(t)<0. (2.18)

    ⅳ. The system is stable with an input |u|γ1|Gx|.

    Proof: Since (ETVTxϵϕu(t))Tϵ1ϕ(ETVTxϵϕu(t))0

    ϵ1ϕVxEETVTx+uT(t)ETVTx+VxEu(t)ϵϕuT(t)u(t)0. (2.19)

    Adding Eq (2.19) to Eq (2.17) yields Eq (2.18); hence, ii iii. If iii is valid, substituting u(t)=ETVTx into Eq (2.18) yields Eq (2.17) (u(t)=ETVTxcorresponds to the value of u that optimizes Eq (2.18)). Hence, ii iii. Now, from Eq (2.13), Ax(t)+AΓx(tΓ)+Eu(t)=˙x(t); substituting Ax(t)+AΓx(tΓ)+Eu(t) with ˙x(t) into Eq (2.18) and integrating the result yields

    V(t,x)+ϵϕt0(yT(t)y(t)γ2uT(t)u(t))dτ<V(0,x(0)). (2.20)

    Thus, when |u|γ|y|, we can choose V(t,x)+ϵϕt0(yT(t)y(t)γ2uT(t)u(t))dτ>0 as our Lyapunov candidate to demonstrate stability. Hence, ⅲ ⅳ.

    When x(0)=ˉ0, V(0,x(0))=0. Since V(t,x)0, we find that Eq (2.20) yields

    t0(yT(t)y(t)γ2uT(t)u(t))dt<0, (2.21)

    or ⅱ-ⅳ ⅰ. To show ⅰⅳ, let us consider |u|γ1|y|. This is equivalent to interconnecting G(s) with another system ||G||γ1. Since ||G||||G||<1, it follows from the small gain theorem [46] that the interconnected system is stable. Thus i ⅳ.

    A time delay can be approximated in the Laplace domain using Kth-degree numerator and denominator polynomials as follows [48]

    Gd(s)={Kk=0ςk(s)k}/{Kk=0ςksk}=(1)K{Kk=0ˉςksk}/{Kk=0ςksk}, (2.22)

    where ςk=(2Kk)!/[ΓKkk!(Kk)!], ˉςk=2ςk if k is even,else 0.

    Notice that owing to symmetry, |Gd(jω)|=1. The state-space representation of the delay can now be constructed such that

    Gd=(1)K+Cd[sIAd]1Bd. (2.23)

    In the controller canonical form, Ad,Bd, and Cd are given by

    Ad=[ˉ0IK1ς0ς1ςK1],Bd=[ˉ01],Cd=[ˉς0ˉςK1]. (2.24)

    If ˜x is known, then the approximation of the delayed measurement xdC˜x(tΓ) is written as

    ˙xd(t)=Adxd(t)+BdC˜x(t). (2.25)

    In order to construct the overall approximate dynamics, we also need to replace ˜x with ˜xa. Hence, the approximate observer dynamics can be written as

    ˙xd(t)=Adxd(t)+BdC˜xa(t)˙˜xa(t)=A˜xa(t)+Fw(t)L[(1)KC˜xa(t)+Cdxd(t)]Lv(t)+E˜ϕ(t,u,ˆxa,˜xa), (2.26)

    where ˜xa is the approximation of ˜x, and ˆxa is the approximation of ˆx. In the extended space,

    ˙z(t)=(ˉAˉLˉC)z(t)+ˉE˜ϕ(t,u,ˆx,˜x)+ˉFw(t)ˉLv(t), (2.27)

    where

    z=[xd˜xa],ˉA=[AdBdC0A],ˉC=[Cd(1)KC], (2.28)
    ˉL=TL,ˉE=TE,ˉFext=TFext,ˉG=GTT, (2.29)
    T=[ˉ0Kny×nIn×n]. (2.30)

    This can be easily extended to multiple sensors by stacking the state-space representation of the delay for the individual sensors.

    Let us define the approximation error as

    δGd(s):=(Gd(s)Gd(s))G1d(s)=Gd(s)G1d(s)1. (2.31)

    Figure 1 plots |δGd(jω)| vs. Γω for various values of K.

    Figure 1.  Plot of the magnitude of δGd(s=jω) for different values of K.

    Lemma 5: For δGd(s) defined by Eq (2.31), T defined by Eq (2.30), ˉA,ˉL, and ˉC defined by Eqs (2.28) and (2.29), and Ω,Ξ,

    Ω[sIA+LGd(s)C]1Ξ=ΩTT[sI(ˉAˉL(1+δGd(s))ˉC)]1TΞ. (2.32)

    Proof: From Eqs (2.31) and (2.23), we can deduce that

    Gd(s)=(1+δGd(s))Gd(s)=(1+δGd(s))[(1)K+Cd(sIAd)1Bd], (2.33)

    Hence,

    sIA+LGd(s)C=(sIA)+L(1+δGd(s))[(1)K+Cd(sIAd)1Bd]C=[sIA+(1)KL(1+δGd(s))C][L(1+δGd(s))Cd](sIAd)1[BdC]. (2.34)

    Using Lemma 3 we find that

    {[sIA+(1)KL(1+δGd(s))C][L(1+δGd(s))Cd](sIAd)1[BdC]}1=TT[sIAdBdCL(1+δGd(s))CdsIA+(1)KL(1+δGd(s))C]1T. (2.35)

    Substituting Eq (2.34) into Eq (2.35) and substituting for ˉA,ˉC, and ˉL from Eq (2.28) yields Eq (2.32).

    We will now determine a bound on the approximation error that can then be used in the observer design. Since |Gd(jω)|=|Gd(jω)|=1, Eq (2.31) yields ||δ(s)||=2. However, from Figure 1, it is clear that if we are only interested in frequencies below a particular threshold, it may be possible to define a lower effective bound for δGd(jω).

    Definition 1: The effective bound on the error of Kth Padé approximation is defined as γC>0, s.t., Ω,Ξ and ˉA,ˉC,and ˉL as defined in Eq (2.28), ˉδGd:||ˉδGd(s)||γC such that,

    ||Ω[sIA+LGd(s)C]1Ξ||=ΩTT[sI(ˉAˉL(1+ˉδGd(s))ˉC)]1TΞ. (2.36)

    Lemma 6: Suppose GNL(s):=Ω[sIA+LGd(s)C]1Ξ, is strictly proper, then

    γCmaxωωδ|δGd(jω)|, (2.37)

    where

    ωδωmaxs.t., ωωδ,|GNL(jω)|||GNL||1+4|ˉL||ˉC|. (2.38)

    Proof: Let ωmaxbe the frequency at which the H norm is reached or |GNL(jωmax)|=||GNL||. Since GNL(s) is strictly proper, |GNL(jω)|0 as ω0. Hence, ωδωmax s.t. ωωδ, |GNL(jω)|||GNL||/(1+4|ˉL||ˉC|). Now, consider

    Θ(ω):=[(jωIˉA)+ˉL(1+δGd(jω))ˉC]1, (2.39)
    ˉδGd(jω)={δGd(jω)ωωδδGd(jωδ)ω>ωδ, (2.40)
    ˉΘ(ω):=[sI(ˉAˉL(1+ˉδGd(s))ˉC)]1. (2.41)

    Let ΔδGd=ˉδGd(jω)δGd. Hence

    ˉΘ(ω):=[(jωIˉA)+ˉL(1+δGd(jω)+ΔδGd)ˉC]1. (2.42)

    Now

    (jωIˉA)+ˉL(1+δGd(jω))ˉC=(jωIˉA)+ˉL(1+δGd(jω)+ΔδGd)ˉCˉLΔδGdˉC. (2.43)

    Substituting Eqs (2.39) and (2.42) into Eq (2.43) and simplifying yields,

    ˉΘ(ω)=[IˉLΔδGdˉC]1Θ(ω)[I+ˉLΔδGdˉC]Θ(ω). (2.44)

    Since ||δ(s)||=2, for ωωδ, |ΔδGd(ω)|4. From Eq (2.38), ˉΘ(ω)<||GNL||ωωδ. Hence, we are only interested in |ˉδGd(jω)| for ωωδ. Since for ωωδ, ˉδGd(jω)=δGd(jω), it follows that γC|δGd(jωδ)|.

    To illustrate the utility of Lemma 6, suppose it is known that ωδ=0.1/Γ, then for K=2, the effective bound on |δGd(jω)| is 2×1010.

    Note: We can choose ωδRe(λminˉAˉLˉC).

    Note: In principle, γC can be made arbitrarily small by increasing K. However, notice that ς0=(2K)!/[ΓKK!], and ςK1=(K+1)K/Γ; as we increase K, the variation between the smallest and the largest values of ςk will increase and ˉA and ˉC will become less well conditioned. While there is no strict upper limit for K, a very large K may cause computation problems.

    Theorem 7: The noise-free, disturbance-free, and uncertainty-free nominal error dynamics, Eq (2.6) with w=0,v=0 and [ΔAΔEΔH]=0, is stable ϕ satisfying Eq (2.4) if, for the Kth Padé approximation of the delay, ˉPR(n+Kny)×(n+Kny)>0,ϵϕ>0, and ϵC>0 s.t.,

    [NˉPˉEˉPˉLˉETˉPϵϕIˉ0ˉLTPˉ0ϵCI]<0, (2.45)

    where

    N=ˉATˉP+ˉPˉAˉPˉLˉCˉCTˉLTP+ϵϕˉGTˉG+ϵCγ2CˉCTˉC, (2.46)

    and ˉ0-s are zero matrices of appropriate dimensions, ˉA,ˉC,ˉL,ˉE and ˉG are defined in Eq (2.28), and γC>0 is the effective bound on the error dynamics as defined in Definition 1.

    Further, conditions Eq (2.45) become necessary and sufficient as γC0.

    Proof: For Ξ=E and Ω=G in Definition 1, ||ˉδGd(s)||γC

    ||G[sIA+LGd(s)C]1E||=ˉG[sI(ˉAˉL(1+ˉδGd(s))ˉC)]1ˉE. (2.47)

    For such a ˉδGd, the state-space form of ˉG[sI(ˉAˉL(1+ˉδGd(s))ˉC)]1ˉE can be written as

    ˙zeq(t)=(ˉAˉLˉC)zeq(t)ˉLyˉδ(t)+ˉE˜ueq(t), (2.48)
    yˉδ(t)=L1[ˉδGd(s)L(ˉCzeq(t))],||ˉδGd(s)||γC, (2.49)
    ˜yeq(t)=ˉGzeq(t). (2.50)

    Multiplying Eq (2.45) by [zTeq ˜uTeq yTˉδ] on the left and col(zeq˜ueqyˉδ) on the right,

    ˙V:=[zeq(t)˜ueq(t)yˉδ(t)]T[NˉPˉEˉPˉLˉETˉPϵϕI0ˉLTP0ϵCI][zeq(t)˜ueq(t)yˉδ(t)]<0. (2.51)

    Noting that ˉAzeq(t)ˉLˉCzeq(t)ˉLyˉδ(t)+ˉE˜ueq(t)=˙zeq(t) (from Eq (2.48)), we find

    zTeq(t)ˉP˙zeq(t)+˙zTeq(t)ˉPzeq(t)+ϵϕ[˜yTeq(t)˜yeq(t)˜uTeq(t)˜ueq(t)]        +ϵC[γ2CzTeq(t)ˉCTˉCzeq(t)yTˉδ(t)yˉδ(t)]<0. (2.52)

    Integrating the above equation yields

    zTeq(t)ˉPzeq(t)+ϵϕt0[˜yTeq(τ)˜yeq(τ)˜uTeq(τ)˜ueq(τ)]dτ+ϵCt0[γ2CzTeq(t)ˉCTˉCzeq(t)yTˉδ(τ)yˉδ(τ)]dτ+zTeq(0)ˉPzeq(0)<0. (2.53)

    Since ||ˉδGd(s)||γC,

    t0[yTˉδ(τ)yˉδ(τ)γ2CzTeq(t)ˉCTˉCzeq(t)]dτ0. (2.54)

    Applying Lemma 1 to Eqs (2.53) and (2.54), we find

    zTeq(t)ˉPzeq(t)+ϵϕt0[˜yTeq(τ)˜yeq(τ)˜uTeq(τ)˜ueq(τ)]dτ+zTeq(0)ˉPzeq(0)<0. (2.55)

    Thus when zeq(0)=0, t0˜yTeq(τ)˜yeq(τ)dτ<t0˜uTeq(τ)˜ueq(τ)dτ or,

    ˉG[sI(ˉAˉL((1)K+ˉδGd(s))ˉC)]1ˉE<1. (2.56)

    From Eqs (2.47) and (2.57), we can deduce that

    ||G[sIA+LGd(s)C]1E||<1. (2.57)

    Replacing u with ˜ϕ and AΓ with LC, in Lemma 4, it is clear that Eq (2.57) V=V(t,˜x):R×RnR satisfying V(t,˜x)>0 ˜xˉ0,V(t,ˉ0)=0, and ϵϕR0 s.t ˜x,˜ϕ

    ddt{V(t,˜x)+ϵϕt0[˜xT(t)GTG˜x(t)˜ϕT(t,u,ˆx,˜x)˜ϕ(t,u,ˆx,˜x)]dτ}<0. (2.58)

    Since ϕ(t,u,ˆx,˜x) satisfies Eq (2.4),

    t0[˜xT(t)GTG˜x(t)˜ϕT(t,u,ˆx,˜x)˜ϕ(t,u,ˆx,˜x)]dτ0. (2.59)

    Let us define a candidate Lyapunov function for the system Eq (2.6) as

    V||V(t,˜x)+ϵϕt0[˜xT(t)GTG˜x(t)˜ϕT(t,u,ˆx,˜x)˜ϕ(t,u,ˆx,˜x)]dτ>0. (2.60)

    Since V>0 ˜xˉ0,and ˙V<0, the system Eq (2.6) is stable.

    On the limit γC0,

    ||G[sIA+LGd(s)C]1E||=||ˉG[sI(ˉAˉLˉC)]1ˉE||. (2.61)

    If on the limit γC0, ˉP satisfying Eq (2.45), then ||G[sIA+LGdC]1E||>1. Subsequently, ϕ, for instance ϕ=sin(Gx) that would make the observer unstable. Hence the condition becomes necessary when γC0.

    Note: It is clear from the above theorem that we need to make γC as small as possible. Without the concept of effective bound, however, we would need to use γC=2 (since ˉδ=δ and ||δ(s)||=2).

    Lemma 8: Given weighting matrices WRn×n, RextRnwext×nwext, and QRny×ny, the observer Eq (2.5) can eventually guarantee the following performance

    ttT˜xT(τ)W˜x(τ)dτttT(vT(τ)Qv(τ)+wText(τ)Rextwext(τ))dτ, (2.62)

    for all nonlinearities ϕ satisfying Eq (2.4), if for the Kth Padé approximation of the delay ˉP>0,ϵϕ>0,ϵC>0, and ϵW>0 s.t.,

    [NˉPˉEˉPˉFextˉPˉLˉPˉLˉETˉPϵϕIˉ0ˉ0ˉ0ˉFTextˉP0ϵWRˉ0ˉ0ˉLTPˉ0ˉ0ϵWQˉ0ˉLTPˉ0ˉ0ˉ0ϵCI]<0, (2.63)

    where N=ϵWTWTWTT+ˉATˉP+ˉPˉAˉPˉLˉCˉCTˉLTP+ϵϕγ2ϕˉGTˉG+ϵCγ2CˉCTˉC, ˉ0 is a zero matrix, ˉA,ˉC,ˉL,ˉE,and ˉG are the systems matrices for the error dynamics defined in Eq (2.28), and γC>0 is the effective bound on the approximation error as defined in Definition 1.

    Further, Eq (2.63) becomes necessary and sufficient on the limit γC0.

    Proof: Let E:=[EFext(ϵW/ϵϕR)1/2L(ϵW/ϵϕQ)1/2] and G:=col(G,(ϵW/ϵϕ)1/2W). By multiplying Eq (2.63) by diag(I,I,(ϵWϵϕR)1/2,(ϵWϵϕQ)1/2,I) on both sides, we find

    [NˉPTEˉPˉLETTTˉPϵϕ[Iˉ0ˉ0ˉ0Iˉ0ˉ0ˉ0I][ˉ0ˉ0ˉ0]ˉLTP[ˉ0ˉ0ˉ0]ϵCI]<0. (2.64)

    Following the proof of Theorem 7, we can show that

    ||G[sIA+LGd(s)C]1E||<1. (2.65)

    Replacing u with col(˜ϕ,(ϵWϵϕQ)1/2v,(ϵW/ϵϕRext)1/2wext), G with G and E with E in Lemma 4, we find V satisfying Eq (2.14), s.t.,

    ˙V+ϵϕ[˜xTGTG˜x˜ϕT˜ϕ]+ϵW[˜xTW˜xvTQvwTextRextwext]<0. (2.66)

    Hence, if we define

    VI(t,˜x)||V(t,˜x)>0,V=ttTVI(τ,˜x)dτ. (2.67)

    Notice that ˙V<0 when

    ttT˜xT(τ)W˜x(τ)dτ>ttT[vTQv+wTextRextwext]dτ. (2.68)

    Hence, from Lemma 2, the system will converge to

    ttT˜xT(τ)W˜x(τ)dτttT(vTQv+wTextRextwext)dτ. (2.69)

    It also follows from the proof of Theorem 7 that the condition becomes necessary when γC0.

    Note: While not strictly necessary, if w and v are zero-mean white Gaussian noises, then we can choose (R)1 equal to the covariances of v and (Q)1 equal to the covariances of w.

    Notice that Eq (2.63) ceases to be a linear matrix inequality if L is unknown. Hence, we will require an iterative procedure for determining L. Such an iterative method would require a method of evaluating the fitness of a chosen L.

    Lemma 9: For the system

    ˙zeq(t)=(ˉAˉLˉC)zeq(t)+ˉFextwext(t)ˉLyˉδ(t)ˉLv(t)+ˉE˜ueq(t)yˉδ(t)=L1[δGd(s)L(ˉCZeq(t))],||ˉδGd(s)||γC˜yeq(t)=ˉGzeq(t). (2.70)

    If ˉP:IˉP>0,ϵϕ>0,ϵC>0andϵW>0, s.t.,

    [NˉPˉEˉPˉLˉPˉLˉPˉFextˉETˉPI000ˉLTP0ϵCI00ˉLTP00ϵWQ10ˉFTextˉP000ϵWR1]<0, (2.71)

    where N=ϵWTWTT+ˉATˉP+ˉPˉAˉPˉLˉCˉCTˉLTP+ˉGTˉG+ϵCγ2CˉCTˉC+γSI, then if γS>0, system Eq (2.70) has the performance

    ttTzTeq(τ)TWTTzeq(τ)dτttT(vT(τ)Rv(τ)+wT(τ)Qw(τ))dτ, (2.72)

    with an exponential rate γS.

    Proof: Defining ˙V by multiplying Eq (2.71) by [zTeq ˜ϕTeq yTˉδ vTwT] on the left and col(zeq,˜ϕeq,yˉδ,v,w) on the right, and following the proof of Theorem 7, we can show that

    ddt[˜xT(t)ˉP˜x(t)]+ϵW[zTeq(t)TWTTzeq(t)(vT(t)Rv(t)+wT(t)Qw(t))]<γS˜xT(t)˜x(t). (2.73)

    Since IˉP, it is evident that Eq (2.73) ensures that for V(t)=1TttT˜xT(τ)ˉP˜x(τ)dτ, when Eq (2.72) is not satisfied

    ˙V<γSV, (2.74)

    Q.E.D.

    For numerical stability, we may need to impose the constraint ˉP105I and iteratively solve Eq (2.71) and move in the direction of increasing γS until γS>0.

    We will use a simple example to show that the proposed observer design works better than other algorithms from the literature. Consider a second-order unstable system with a dead-zone nonlinearity.

    ˙x(t)=[0113]x(t)+[01]ϕ(x1)+[01]uy(t)=[10]x(t0.5), (3.1)
    ϕ(x1)={10(x1+xdead)ifx1<xdead0ifxdeadx1xdead10(x1xdead)ifx1>xdead. (3.2)

    Notice |ϕ(x1a)ϕ(x1b)5(x1ax1b)|5|x1ax1b|. Hence,

    A=[0143],B=[01],C=[10],E=[01],F=[00],G=[50],Γ=0.5,R=0,Q=0. (3.3)

    The parameters of this system were chosen arbitrarily such that the eigenvalues of A are stable while those of A+EG are unstable. Assuming that our observer eigenvalues would be of the same order of magnitude as our open-loop system, Γ×|Re(λmax)| 5. From Figure 1, let us choose K = 6 with γ=105. We can solve the equation to get L=[2.2  2.6]T, with

    ˉP=102×[7.281.243.346.032.063.161.600.101.2416.1417.544.0114.064.093.770.773.3417.5441.431.4137.921.812.252.746.034.011.4117.400.2711.093.571.802.0614.0637.920.2736.221.903.702.083.164.091.8111.091.909.033.630.411.603.772.253.573.703.6315.049.590.100.772.741.802.080.419.598.90]. (3.4)

    Since much of the literature on delay observers has focused on linear systems, let us consider an equivalent linear system with xdead=0, wherein Aeq=A+EG. The eigenvalues of Aeq are 1.8541 and -4.8541, and the system is unstable without feedback. We know that for a given L, the linear observer would be stable if all of the eigenvalues of the characteristic equations det(sIA+LeΓsC)=0 have negative real parts. Since L only has two elements, we have solved for the roots of the characteristic equations for different values of L. Figure 2 plots the maximum of the real part of the eigenvalues (either the least negative or the most positive eigenvalue), as well as the stability boundary wherein this maximum of the real part of the eigenvalues is zero.

    Figure 2.  (a) 3D plot of Re(λ). (b) Observer stability boundary.

    Figure 3 plots the stability boundary obtained by applying the observer design proposed in this paper. This boundary matches the theoretical stability boundary in Figure 2.

    Figure 3.  Observer gain stability boundary of the proposed algorithm.

    We are unable to make a direct comparison to literature results as there are no equivalent explicit procedures for observer design for systems with measurement delay. Hence, we select possible observer gains over a dense grid (L=[l1l2]T, 100l1100, and 100l2100). For each L, we examined the feasibility of the LMI that was proposed in Fridman and Shaked [44,49], by attempting to find P1>0, P2,P3,P4, s.t.,

    M=[(AeqLC)TP2+P2(AeqLC)P1PT2+(AeqLC)TP3ΓPT2LC(P1PT2+(AeqLC)TP3)TP3PT3+ΓP4ΓPT3LC(ΓPT2LC)T(ΓPT3LC)TΓP4]<0. (3.5)

    As we were unable to find any L, we aimed to get a better understanding of feasibility by making M<α under the constraint P1105I (without this constraint, it is possible to make P1, P2,P3,P4 arbitrarily small, which will lead to numerical problems). Figure 4 plots the minimum α for L=[l1l2]T, 100l1100, and 100l2100 (the plot was generated by minimizing α using LMI solver for every combination of l1 and l2 in the given range). It is seen that α>0, indicating that an observer design is not possible.

    Figure 4.  Observer design based on Fridman and Shaked [44,49].

    We similarly attempted to solve the following LMI that was proposed by Park et al. [50] (presented in Fridman [44]), P1>0, P2>0,P3>0, s.t.,

    [ATeqP1+P1Aeq+P3P2P1LC+P2ΓATeqP2(LC)TP1+P2P2P3Γ(LC)TP2ΓP2AeqΓP2LCP2]<0. (3.6)

    The LMI was similarly found to be infeasible. This is expected since the condition based on earlier literature requires the eigenvalues of ΓLC to lie within a unit circle [44], while Figure 2 indicates a different stability boundary. We were similarly unable to design a nonlinear observer for the system based on the methodology proposed in Targui et al. [40], as for every L, we were unable to find P>0 such that

    [(AeqLC)TP+P(AeqLC)+ϵ2ΓIΓPLCAeq(ΓPLCAeq)Tϵ2ΓI]<0. (3.7)

    In this example, we consider an unstable nonlinear system with

    A=[010048.61.2548.60000120019.51.25],B=[021.600],E=[0000.333],F=B,C=[1000],ϕ(x,u)=sin(x3). (3.8)

    This system is an unstable variation of the elastic joint robotic arm studied in the literature [6,24]. Notice that the eigenvalues of A are 0.2385,0.6250±8.2501j,1.4885. The real system is assumed to have a mass that is 1% lower than the nominal system. This would result in

    ΔA=[00000.4860.01250.486000010.200.1950.0125],ΔB=[00.021600],ΔE=[0000.00333]. (3.9)

    We will further assume that the input disturbance is given byw (0,102), the noise in the sensor is given by v (0,102), and the sensor measurements are delayed by Γ=0.1s. If we solve for L by ignoring the delay, we can obtain

    Lignore delay=[15.59121.5610.5022.06]T. (3.10)

    Notice that ||E(sIA+Lignore delayC)G||<1, and the observer is stable in the absence of delay. However, Figure 5 shows that the observer is unstable (when by u=sin(t)).

    Figure 5.  Observer design ignoring delay.

    We will use K=3 and γC=105, and set W=I. Applying the proposed algorithm for a delay time of 0.1 s on outputs, we obtain:

    L=[7.67089.82570.26641.4747]T. (3.11)

    Figure 6 shows the observer states, and Figure 7 shows that the performance ratio ttT˜xT(τ)W˜x(τ)dτ/ttT(vT(τ)Qv(τ)+wText(τ)Rextwext(τ))dτ is less than 1.

    Figure 6.  Proposed observer design: States.
    Figure 7.  Proposed observer design: Performance ratio.

    This section discusses potential stability issues with the nonlinear delay observer that was proposed in Targui et al. [40]. Targui et al. [40] considered a noise-free system and an observer of the form

    ˙x(t)=Ax(t)+ϕ(t,u,x)+Hu(t), (4.1)
    y(t)=Cx(tΓ), (4.2)
    ˙ˆx(t)=Aˆx(t)+L[y(t)+CttΓ(Aˆx(τ)+ϕ(τ,u,x)+Hu(τ))dτCˆx]+ϕ(t,u,ˆx)+Hu(t). (4.3)

    The error dynamics of ˜x=xˆx is given by Eqs (4.1)-(4.3)

    ˙ˆx(t)=(ALC)˜x(t)+L[Cx(tΓ)Cx+CttΓ(Aˆx(τ)+ϕ(τ,u,x)+Hu(τ))dτ]+ϕ(t,u,x)ϕ(t,u,ˆx). (4.4)

    Using

    C[xx(tΓ)]=CttΓ(Ax(τ)+ϕ(τ,u,x)+Hu(τ))dτ. (4.5)

    They obtain

    ˙˜x(t)=(ALC)˜x+ϕ(t,u,x)ϕ(t,u,ˆx)+LCttΓ(A˜x(τ)+ϕ(τ,u,x)ϕ(τ,u,ˆx))dτ. (4.6)

    Using the following LK functional

    V=˜xTP˜x+tΓtθ˜xT(t+ψ)[ϵ2I+ϵ3GTG]˜x(t+ψ)dθdψ. (4.7)

    The authors show that the observer design would be stable if

    [NPΓPLCAΓPLCPϵ1I00(ΓPLCA)T0Γϵ2I0(ΓPLC)T00Γϵ3I]<0N=P(ALC)+(ALC)TP+ϵ1GTG+Γϵ2I+Γϵ3GTG. (4.8)

    We will now provide an example where the error dynamics could be unstable despite satisfying Eq (4.8). Consider the unstable linear system

    ˙x=x+uy=x(t0.75). (4.9)

    Here, A=1, C=1, G=0 and Γ=0.75. We can construct an observer of the form Eq (4.3)

    ˙ˆx(t)=ˆx(t)+L[y(t)+ttΓ(Aˆx(τ)+u(τ))dτˆx]. (4.10)

    Since G=0, we can set ϵ1,ϵ3. Notice Eq (4.14) is satisfied for l=5, and P=1,ϵ2=5,ϵ1,ϵ3, since

    [4.253.753.753.75]<0. (4.11)

    Hence, the observer should be stable. Figure 8 shows the error dynamics for the above system for u=sin(100t). It is evident from the figure that the observer is unstable, and the observer design proposed by Targui et al. [40] may not be viable.

    Figure 8.  Observer design based on Targui et al. [40].

    We will provide further analysis to get to the root of the problem. Let us begin by considering a more general form of the above system using A=a, L=l, G=0.

    ˙x=ax+uy=x(t0.75). (4.12)
    ˙ˆx(t)=ˆx(t)+l[y(t)+ttΓ(Aˆx(τ)+u(τ))dτˆx]. (4.13)

    As before, since G=0, we can set ϵ1,ϵ3. Hence, Eq (4.8) can be written as

    [2Pa2Pl+ϵ2ΓΓPlaΓPlaϵ2Γ]<0. (4.14)

    Taking Schur's complement, we find that we need

    M:=2Pa2Pl+ϵ2Γ+Γϵ12(Pla)2<0. (4.15)

    Setting dM/dϵ2=0 yields

    ΓΓϵ22(Pla)2=0 or ϵ2=P|la|. (4.16)

    Notice that ϵ2=P|la| is a minimum as d2M/dϵ22>0. Hence, substituting in Eq (4.15) we find

    Mminϵ2=2Pa2Pl+2P|la|Γ<0. (4.17)

    Since P>0, we need

    al+(|la|)Γ<0. (4.18)

    Since l would usually be positive, we need

    l>a1|a|Γ. (4.19)

    There does not seem to be an upper bound for stability (which contradicts our understanding of system delay). It should be noted that even if the observer would work under most normal circumstances, it would be extremely difficult to implement.

    This paper has developed an observer design procedure for a nonlinear system in the presence of unknown input disturbance, sensor delay, and sensor noise. Necessary and sufficient conditions have been presented for the observer stability in the form of linear matrix inequalities. The observer design procedure was demonstrated for a simple 2D system and an elastic joint robotic arm with delay. Additionally, the paper provided a means of calculating the H gain of the state-space representation of a delay system, which can be extended to a robust control of the system with delay.

    The proposed design procedure is less conservative than results that use Lyapunov–Krasowskii (LK) or Lyapunov–Razumikhin (LR) functionals. In the future, the proposed observer design may be extended to systems with variable delay and to hybrid systems by treating the discrete-time digital signal as a delayed measurement. Additionally, controller and observer designs are often dual problems, and it will be possible to adapt these results to several controller designs.

    The author declares that they have not used any Artificial Intelligence (AI) tools in the creation of this article.

    The author would like to thank Alireza Beigi, Divya Rao Ashok Kumar, and Amirmasoud Ghasemi Toudeshki at SFU, for their assistance with a few of the simulations used in the paper. The authors would like to acknowledge funding support from NSERC through the Discovery grant (RGPIN/02971-2021).

    The author declares that there is no conflict of interest.



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