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adaptive dynamic programming-Based Finite-Time Optimal Backstepping Force/Position Control of Reconfigurable Robot Manipulators via Pareto Optimal

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10660-10671页

作者： Wang, Yuexi An, Tianjiao Dong, Bo Zhu, Mingchao Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

To address the force/position control challenges in transitioning from free-space motion to tasks involving environmental contact, this paper proposes an adaptive dynamic programming (ADP)-based finite-time optimal backstepping force/position control method for Reconfigurable Robot Manipulators (RRMs), which ensures rapid convergence of state errors under external constraints while maintaining system stability. By integrating robust control, the proposed method enhances both convergence speed and robustness against uncertainties. Furthermore, the parameters related to robustness are optimized using a cooperative game-theoretic approach based on Pareto optimality. A Lyapunov-based analysis demonstrates the closed-loop system's Semi-Global Practical Finite-time Stability (SGPFS). Experimental validation confirms the effectiveness of the proposed control method. Note to Practitioners-In applications such as space operations, deep-sea exploration, and disaster rescue, RRMs must smoothly transition from free-space movement to tasks involving physical contact with external environments. Traditional force/position control methods often struggle to maintain stability and achieve rapid convergence under these conditions. This paper introduces a control strategy that combines ADP with backstepping to achieve finite-time optimal control. The proposed approach ensures stability, fast convergence, and robustness to uncertainties, addressing practical needs for energy efficiency and reliable force/position tracking. Moreover, the method optimizes control parameters through a cooperative game-theoretic framework based on Pareto optimality, balancing control effort and the convergence domain size. Experimental results confirm the practical effectiveness of the proposed strategy in complex environments.

关键词： Convergence Uncertainty Robust control Optimal control Backstepping Automation Manipulator dynamics Games dynamics Vectors Reconfigurable robot manipulators adaptive dynamic programming force/position control optimal finite-time control Pareto optimal

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adaptive dynamic programming based event-triggered multi-H∞ control

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NEUROCOMPUTING 2025年 638卷

作者： Xue, Shan Liu, Zhe Wang, Liqi Zhang, Weidong Ren, Ke Yang, Xinhui Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Minist Educ Engn Res Ctr Marine Intelligent Syst Harbin Peoples R China Huaneng Hainan Changjiang Nucl Power Co Ltd Changjiang Peoples R China

This article presents an adaptive dynamic programming (ADP) based event-triggered multi-H infinity control method for the completely unknown systems. First, a weighted average method is used to ensure balanced disturbance resistance for each input. Next, neural networks (NNs) are employed to identify the dynamics of completely unknown systems. Then, critic NNs are used to approximate value functions to achieve Nash equilibrium . Additionally, event-triggered control and disturbance strategies with a new threshold are developed. Finally, the method's effectiveness is validated through two numerical experiments.

关键词： adaptive dynamic programming H infinity control Neural network Event-triggered mechanism

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adaptive dynamic programming for a Nonlinear Two-Player Non-Zero-Sum Differential Game With State and Input Constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第3期35卷 1117-1133页

作者： Zhou, Yinglu Li, Yinya Sheng, Andong Qi, Guoqing Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China

This paper investigates a nonlinear two-player non-zero-sum differential game with state and input constraints. To solve this problem, this paper constructs a neural network (NN) framework to approximate the solution of the Hamilton-Jacobi-Isaacs (HJI) equation. The adaptive dynamic programming (ADP) method is utilized where each player only needs one critic NN. To solve the issue of state and input saturations, this paper develops a novel constrained system for the differential game, firstly to make the states within the predetermined constraint set. Then, the non-quadratic expression is used to substitute the traditional quadratic expression for the two-player non-zero-sum differential game, and both of the inputs of the two players are constrained. With these treatments, the control input and the system are more in line with real-world applications. Moreover, the stability of the system is also analyzed using the Lyapunov theorem. Two numerical examples are presented to illustrate that the critic NN weights estimation errors and the system are uniformly ultimately bounded (UUB), and the state and input constraints can be achieved.

关键词： adaptive dynamic programming input constraints non-zero-sum differential game state constraints

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Finite-horizon approximate optimal attitude control based on adaptive dynamic programming for ultra-low-orbit satellite

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ADVANCES IN SPACE RESEARCH 2025年第4期75卷 3856-3869页

作者： Ma, Jiahong Wu, Baolin Geng, Yunhai Wang, Menglei Harbin Inst Technol Res Ctr Satellite Technol Harbin 150080 Peoples R China State Key Lab Microspacecraft Rapid Design & Intel Harbin 150080 Peoples R China

This study investigates the finite-horizon approximate optimal attitude control problem for ultra-low-orbit satellites, addressing the complexities introduced by substantial disturbance, actuator faults, actuator saturation, and time constraint. Initially, a fixed-time concurrent learning fault and disturbance estimation approach is proposed that relieves persistent excitation constraints and isolates different influences individually. Subsequently, the cost function is designed with actuator fault estimation, ensuring that the control strategy consistently adheres to actuator saturation constraints and can compensate for current faults. Furthermore, based on the adaptive dynamic programming, an approximate optimal attitude control approach is proposed, which employs time-varying activation functions to approximate the optimal cost function. A fixed-time neural network weight adaptation strategy is designed to ensure the precision and reliability of the approximation. Finally, the numerical simulation confirms the validity and practical applicability of the proposed approach in satellite attitude control systems. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Attitude control adaptive dynamic programming Disturbance and actuator faults Actuator saturation

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Zero-sum games-based optimal fault tolerant control for control-constrained multiplayer systems with external disturbances via adaptive dynamic programming

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 147卷

作者： Liu, Shihui Xu, Ning Li, Lun Alharbi, Khalid H. Zhao, Xudong Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China Bohai Univ Coll Informat Sci & Technol Jinzhou 121013 Liaoning Peoples R China Weifang Univ Coll Machinery & Automat Weifang 261061 Shandong Peoples R China King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Commun Syst & Networks Res Grp Jeddah Saudi Arabia

In this paper, the optimal fault tolerant control problem is investigated for constrained multiplayer systems with external disturbances and completely unknown dynamics. According to games theory, each control player along with its corresponding fault player and disturbance player can be regarded as a two-player zero-sum game, respectively. To release the restriction on the system dynamics, a neural network-based system identification technique is employed to reconstruct the completely unknown system models. A new nonquadratic function is constructed to incorporate the control constraints into the optimization, and the corresponding constrained Hamilton-Jacobi-Isaacs equations (HJIEs) are derived. Then, the constrained HJIEs are solved by adopting a single critic neural network, where an experience replay technique is utilized to exclude the persistence of excitation condition. Furthermore, all signals of the close-loop system are proven to be uniformly ultimately bounded based on the Lyapunov stability theory. Finally, two examples are presented to verify the effectiveness of the developed optimal zero-sum games scheme.

关键词： Fault tolerant control Multiplayer zero-sum games Constrained multiplayer systems Completely unknown dynamics adaptive dynamic programming

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Primal-dual adaptive dynamic programming for finite-horizon optimal control of nonlinear systems with isoperimetric constraints

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AUTOMATICA 2025年 173卷

作者： Wei, Qinglai Li, Tao Zhang, Jie Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Peoples R China

In this paper, a novel primal-dual adaptive dynamic programming (PDADP) method is developed to solve finite-horizon optimal control problems (OCPs) with isoperimetric constraints for continuoustime nonlinear systems. The OCP with isoperimetric constraints is approximated by a series of linear quadratic time-varying OCPs with isoperimetric quadratic constraints, which are solved by the primal- dual (PD) method. The convergence of the PDADP method is proven. Furthermore, the optimality of the solution is analyzed by deriving the necessary optimality conditions via Pontryagin's principle and proving that the limiting values of the iterations satisfy the necessary optimality conditions. Finally, simulation experiments are provided to show the effectiveness of the present PDADP method. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： adaptive dynamic programming Isoperimetric constraints Nonlinear systems Optimal control

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Event-triggered Decentralized H∞ Control for Nonlinear Interconnected System Based on adaptive dynamic programming: Application to Roller Kiln

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第1期23卷 196-211页

作者： Chen, Ning Xiang, Yang Chen, Jiayao Luo, Biao Li, Binyan Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Changsha Univ Sci Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China

During the battery cathode materials preparation, the temperature correlation, the external environment disturbance, and the system instability caused by control updating widely exist. All these make it difficult to accurately control the temperature of roller kiln. For this reason, an event-triggered decentralized H infinity control method based on adaptive dynamic programming is proposed. First, the temperature interconnection model is established by describing the relationship between temperatures of the atmosphere outlet and each temperature zone. Then, as for temperature interconnection and disturbance, an auxiliary subsystem is introduced and a cost function including upper bound of interconnection term, temperature state, control input, auxiliary control law and disturbance is designed. Next, the event-trigger mechanism is introduced. The event-triggering condition is designed by considering the temperature interconnection, temperature state, control input and disturbance. It is proved that the temperature event-triggered decentralized H infinity control problem can be converted to solve the Hamilton-Jacobi-Isaacs (HJI) equation problem of a set of auxiliary subsystems and the critic learning method is used to solve the HJI equation. The state of the auxiliary subsystem and the pulse dynamic system are proved to be uniformly ultimately bounded. Finally, the proposed control approach is implemented to roller kiln to prove its validity.

关键词： adaptive dynamic programming decentralized control event-triggered control Hinfinity control roller kiln

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A Review of Unmanned Vehicle Control with adaptive dynamic programming Implementations

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2025年第1期111卷 1-16页

作者： Liu, Hao Yi, Xinning Liu, Deyuan Valavanis, Kimon P. Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China Zhongguancun Lab Beijing 100094 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA

In this review, the optimal control designs via adaptive dynamic programming (ADP) of unmanned vehicles are investigated. Various complex tasks in unmanned systems are addressed as fundamental optimal regulation and tracking control problems related to the position and attitude of vehicles. The optimal control can be obtained by solving the Hamilton-Jacobi-Bellman equation using ADP-based control methods. Neural network implementations and policy iterative ADP algorithms are common approaches in ADP-based control methods, enabling online updates and partially model-free control for unmanned vehicles with various structures. For complexities and uncertain disturbances in unmanned vehicle dynamics, robust ADP-based control methods are proposed, including robust ADP control for matched and unmatched uncertainties, robust guaranteed cost control with ADP, and ADP-based H infinity\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_\infty $$\end{document} control. In order to reduce communication and computational costs in unmanned vehicle operations, a preliminary discussion on event-triggered optimal control using ADP-based control methods is presented.

关键词： Unmanned vehicles adaptive dynamic programming Optimal control Robust control Event-triggered mechanism

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Finite-time adaptive dynamic programming control for nonlinear systems based on novel complementary terminal sliding mode surface

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2025年第2期239卷 290-302页

作者： Jia, Chao Li, Xinyu Liu, Xiaohua Shangguan, Xuanyue Tianjin Univ Technol Sch Elect Engn & Automat 391 Binshuixi Rd Tianjin 300191 Peoples R China

For a nonlinear system, a novel complementary terminal sliding mode surface-based finite-time adaptive dynamic programming optimal control scheme (FT-NCTSMS-ADP) is proposed. To begin with, with the aim to improve convergence speed and tracking precision, a novel complementary terminal sliding mode surface (NCTSMS) is developed. Then, a specific cost function related to the NCTSMS is developed, and the initial control problem is then transformed toward a series of optimal control problems. The Hamilton-Jacobi-Bellman (HJB) problem is then solved by a single critic neural network, yielding the estimated optimal controller. Finally, using the finite-time lemma, we design a Lyapunov function and verify the system's fast finite-time convergence. Both the simulation and the real-world experiment validate the practical significance of the proposed method.

关键词： Finite-time adaptive dynamic programming complementary terminal sliding mode optimal control

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Human-Machine Shared Stabilization Control Based on Safe adaptive dynamic programming With Bounded Rationality

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第0期

作者： Tan, Junkai Wang, Jingcheng Xue, Shuangsi Cao, Hui Li, Huan Guo, Zihang Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Smart Grid Xian Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China Power Stn Inst New Energy Tech Supervis Dept Tech Supervis Xian Peoples R China

This article considers the shared control of bounded rational human behavior with cooperative autonomous machines. For the collaboration of humans and machines, it is crucial to ensure the safety of the interactive process due to the involvement of human beings. First, a barrier-function-based state transformation is developed to ensure full state safety constraints. A level-k$$ k $$ thinking framework is exploited to obtain bounded rationality. Every single level-k$$ k $$ control policy is approximated by using adaptive dynamic programming. Inspired by the theory of human behavior modeling, a probabilistic distribution based on Softmax is utilized to model human behavior, which imitates the uncertainty of human intelligence in the cooperative game. Through the construction of a shared control framework, the control inputs of humans and machines are blended to achieve stabilization safely and efficiently. Finally, simulations are implemented to test the effectiveness of the proposed cooperation architecture. The result demonstrates that full-state asymmetric constraints and stabilization are guaranteed in commonly safety-critical situations, and the shared control framework ensures the safety of the overall system when one of the participants is not safety-aware.

关键词： adaptive dynamic programming barrier function bounded rationality human-machine collaboration shared control

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