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Event-triggered optimal control for modular reconfigurable manipulators with input constraints based on model predictive control.

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Title: Event-triggered optimal control for modular reconfigurable manipulators with input constraints based on model predictive control.
Authors: Zhou, Fan1 (AUTHOR) zhoufan@ccut.edu.cn, Zhang, Yifan1 (AUTHOR) zyfkzyj08@163.com, Ma, Tianhao2 (AUTHOR) matianhao@nepdi.net
Source: ISA Transactions. Dec2025:Part A, Vol. 167, p839-848. 10p.
Subject Terms: Predictive control systems, Dynamic programming, Discrete systems, Lyapunov stability, Robot control systems, Optimal control theory
Abstract: This paper proposes an event-triggered optimal control method for modular reconfigurable manipulators(MRMs) based on model predictive control(MPC). By using a decentralized optimization method based on MPC, the optimal control problem of MRMs is transformed into independent optimization tasks for each module, while a global MPC optimization framework is utilized to coordinate the modules, ultimately optimizing the overall performance of the entire system. In order to avoid the safety hazards caused by excessive torque, hyperbolic tangent function is added to constrain the input torque. Considering the inaccuracies in the modeling process, adaptive dynamic programming (ADP) is introduced into MPC to improve the robustness of the system. A critical neural network (NN) is designed to solve the Hamilton–Jacobi–Bellman (HJB) equation, yielding the system's optimal solution. Lyapunov theory is used to prove that the trajectory tracking error is uniformly ultimately bounded (UUB). Finally, the comparative experimental results demonstrate that the proposed method achieves significant improvements in reducing tracking error, minimizing resource consumption, and enhancing constrained torque capability. • Event-triggered model predictive control reduces resource use, ensures real-time optimality. • Adaptive dynamic programming enhances model predictive control robustness and adaptability. • Hyperbolic tangent function constrains input torque, ensuring safe and stable operation. • Lyapunov theory proves tracking error is uniformly ultimately bounded, ensuring system stability. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
Description
Abstract:This paper proposes an event-triggered optimal control method for modular reconfigurable manipulators(MRMs) based on model predictive control(MPC). By using a decentralized optimization method based on MPC, the optimal control problem of MRMs is transformed into independent optimization tasks for each module, while a global MPC optimization framework is utilized to coordinate the modules, ultimately optimizing the overall performance of the entire system. In order to avoid the safety hazards caused by excessive torque, hyperbolic tangent function is added to constrain the input torque. Considering the inaccuracies in the modeling process, adaptive dynamic programming (ADP) is introduced into MPC to improve the robustness of the system. A critical neural network (NN) is designed to solve the Hamilton–Jacobi–Bellman (HJB) equation, yielding the system's optimal solution. Lyapunov theory is used to prove that the trajectory tracking error is uniformly ultimately bounded (UUB). Finally, the comparative experimental results demonstrate that the proposed method achieves significant improvements in reducing tracking error, minimizing resource consumption, and enhancing constrained torque capability. • Event-triggered model predictive control reduces resource use, ensures real-time optimality. • Adaptive dynamic programming enhances model predictive control robustness and adaptability. • Hyperbolic tangent function constrains input torque, ensuring safe and stable operation. • Lyapunov theory proves tracking error is uniformly ultimately bounded, ensuring system stability. [ABSTRACT FROM AUTHOR]
ISSN:00190578
DOI:10.1016/j.isatra.2025.08.041