Finite-time surrounding control possesses superior performance in terms of tracking accuracy and rapidity. Nevertheless, most existing finite-time surrounding algorithms are only applicable to integrator systems, restricted to single-target scenarios, and fail to maintain rapid dynamic responses throughout the entire convergence process. To address these limitations, this paper extends the framework to multiple moving targets and generalizes integrators to nonlinear systems with unknown control coefficients. Meanwhile, an accelerated distributed control scheme is developed to improve the convergence performance of each agent throughout the entire process. Specifically, dynamic high-gain distributed observers are constructed for each follower, which achieve target state estimation merely using local relative information. Combined with adaptive techniques, a novel accelerated distributed surrounding protocol based on high-gain is proposed, which realizes finite-time multi-target encirclement and significantly improves global convergence rapidity compared with existing finite-time strategies. The embedded dynamic high-gain mechanism effectively compensates the unknown control coefficients while strictly preserving the fully distributed feature of the protocol, which guarantees strong operational robustness. Numerical simulations further verify the feasibility and effectiveness of the developed control scheme, demonstrating its promising application potential in practical multi-agent cooperative surrounding missions.
Citation: Le Zhao. Accelerated finite-time surrounding control for multiple moving targets[J]. Electronic Research Archive, 2026, 34(12): 9138-9168. doi: 10.3934/era.2026385
Finite-time surrounding control possesses superior performance in terms of tracking accuracy and rapidity. Nevertheless, most existing finite-time surrounding algorithms are only applicable to integrator systems, restricted to single-target scenarios, and fail to maintain rapid dynamic responses throughout the entire convergence process. To address these limitations, this paper extends the framework to multiple moving targets and generalizes integrators to nonlinear systems with unknown control coefficients. Meanwhile, an accelerated distributed control scheme is developed to improve the convergence performance of each agent throughout the entire process. Specifically, dynamic high-gain distributed observers are constructed for each follower, which achieve target state estimation merely using local relative information. Combined with adaptive techniques, a novel accelerated distributed surrounding protocol based on high-gain is proposed, which realizes finite-time multi-target encirclement and significantly improves global convergence rapidity compared with existing finite-time strategies. The embedded dynamic high-gain mechanism effectively compensates the unknown control coefficients while strictly preserving the fully distributed feature of the protocol, which guarantees strong operational robustness. Numerical simulations further verify the feasibility and effectiveness of the developed control scheme, demonstrating its promising application potential in practical multi-agent cooperative surrounding missions.
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Figures(12)
Le Zhao. Accelerated finite-time surrounding control for multiple moving targets[J]. Electronic Research Archive, 2026, 34(12): 9138-9168. doi: 10.3934/era.2026385
Six followers surrounding four moving targets
Graph with six followers and four targets
Encirclement configuration with six followers encircling four moving targets
The trajectories of observer errors $ {{\hat{v}}_{ik1}}-{{v}_{k1}} $, $ i = 1, \cdots, 6, k = 7, \cdots, 10 $
The trajectories of observer errors $ {{\hat{v}}_{ik2}}-{{v}_{k2}} $, $ i = 1, \cdots, 6, k = 7, \cdots, 10 $
The trajectories of observer errors $ {{\hat{x}}_{ik1}}-{{x}_{k1}} $, $ i = 1, \cdots, 6, k = 7, \cdots, 10 $
The trajectories of observer errors $ {{\hat{x}}_{ik2}}-{{x}_{k2}} $, $ i = 1, \cdots, 6, k = 7, \cdots, 10 $
The trajectories of high gains $ {{\gamma }_{ikx}}, {{\gamma }_{ikv}} $, $ i = 1, 2, k = 7, \cdots, 10 $
The trajectories of high gains $ {{\gamma }_{ikx}}, {{\gamma }_{ikv}} $, $ i = 3, 4, k = 7, \cdots, 10 $
The trajectories of high gains $ {{\gamma }_{ikx}}, {{\gamma }_{ikv}} $, $ i = 5, 6, k = 7, \cdots, 10 $
The trajectories of errors $ {{e}_{ivs}} $, high gains $ {{h}_{i}} $, and $ \varrho-2M(t) $, $ i = 1, \cdots, 6, s = 1, 2 $
The trajectories of controls $ {{u}_{is}} $, $ i = 1, \cdots, 6, s = 1, 2$
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