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Modeling and Control Design for a Musculoskeletal Robot via adaptive dynamic programming

IEEE Transactions on Automation Science and Engineering 2025年 22卷 14843-14854页

作者： Song, Yuhua Wan, Weiying Zhu, Lifeng Song, Aiguo School of Instrument Science and Engineering Southeast University State Key Laboratory of Digital Medical Engineering Jiangsu Key Lab of Robot Sensing and Control Nanjing 210096 China Hanglok-Tech Co. Ltd Hengqin 519000 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing 100083 China

In this study, for the musculoskeletal robot system, we aim at optimizing the angle tracking control based on the system model. First, we analyze the driving principle of muscles and establish a bionic muscle dynamics model. Further, based on the geometric relationship between muscles and skeletons, we establish the kinematics model and dynamics model of the musculoskeletal robot system. The adaptive dynamic programming (ADP) algorithm is used for solving the Hamilton-Jacobi-Bellman (HJB) equation. Based on the proposed updating law, the neural network is trained to approximate the solution. Using the Lyapunov’s direct method, we can prove that the system is stable with the proposed controller. Furthermore, simulation indicates that this presented control law is feasible. © 2004-2012 IEEE.

关键词： adaptive Control adaptive dynamic programming dynamic Modeling Musculoskeletal Robot Tracking Control

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adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems

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IET CONTROL THEORY AND APPLICATIONS 2017年第14期11卷 2307-2316页

作者： Yang, Xiong He, Haibo Liu, Derong Zhu, Yuanheng Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The design of robust controllers for continuous-time (CT) non-linear systems with completely unknown non-linearities is a challenging task. The inability to accurately identify the non-linearities online or offline motivates the design of robust controllers using adaptive dynamic programming (ADP). In this study, an ADP-based robust neural control scheme is developed for a class of unknown CT non-linear systems. To begin with, the robust non-linear control problem is converted into a non-linear optimal control problem via constructing a value function for the nominal system. Then an ADP algorithm is developed to solve the non-linear optimal control problem. The ADP algorithm employs actor-critic dual networks to approximate the control policy and the value function, respectively. Based on this architecture, only system data is necessary to update simultaneously the actor neural network (NN) weights and the critic NN weights. Meanwhile, the persistence of excitation assumption is no longer required by using the Monte Carlo integration method. The closed-loop system with unknown non-linearities is demonstrated to be asymptotically stable under the obtained optimal control. Finally, two examples are provided to validate the developed method.

关键词： dynamic programming robust control neurocontrollers continuous time systems control system synthesis nonlinear control systems optimal control function approximation Monte Carlo methods closed loop systems asymptotic stability adaptive dynamic programming robust neural control design unknown continuous-time nonlinear systems CT nonlinear systems ADP-based robust neural control scheme robust nonlinear control problem nonlinear optimal control problem nominal system ADP algorithm actor-critic dual networks control policy approximation value function approximation actor neural network weights critic NN weights Monte Carlo integration method closed-loop system asymptotically stability

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adaptive dynamic programming for decentralized neuro-control of nonlinear systems subject to mismatched interconnections

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OPTIMAL CONTROL APPLICATIONS & METHODS 2022年第5期43卷 1501-1519页

作者： Tang, Yuhong Yang, Xiong Dong, Na Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

This article presents a decentralized neuro-control scheme for a class of continuous-time nonlinear systems with mismatched interconnections through an optimal control method. The decentralized control problem of the studied nonlinear-interconnected systems is transformed into a group of optimal control problems of auxiliary subsystems. First, the value functions of auxiliary subsystems are designed, which are related to the boundaries of interconnected terms. It is proved that, under certain conditions, the decentralized control consisting of optimal control policies of auxiliary subsystems can stabilize the entire interconnected system. Then, an adaptive dynamic programming algorithm is presented to solve the Hamilton-Jacobi-Bellman equations. Critic neural networks (NNs) are employed to approximate the value functions of auxiliary subsystems in order to acquire the optimal control policies. Moreover, the auxiliary subsystems' states and the critic NNs' weight estimation errors are proved to be uniformly ultimately bounded. Finally, a numerical example and an interconnected power system are provided to validate the proposed decentralized neuro-control strategy.

关键词： adaptive dynamic programming decentralized control mismatched interconnection optimal control reinforcement learning

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adaptive dynamic programming for discrete-time linear quadratic regulation based on multirate generalised policy iteration

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INTERNATIONAL JOURNAL OF CONTROL 2018年第6期91卷 1223-1240页

作者： Chun, Tae Yoon Lee, Jae Young Park, Jin Bae Choi, Yoon Ho Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Univ Alberta Dept Comp Sci Edmonton AB Canada Kyonggi Univ Dept Elect Engn Suwon South Korea

In this paper, we propose two multirate generalised policy iteration (GPI) algorithms applied to discrete-time linear quadratic regulation problems. The proposed algorithms are extensions of the existing GPI algorithm that consists of the approximate policy evaluation and policy improvement steps. The two proposed schemes, named heuristic dynamic programming (HDP) and dual HDP (DHP), based on multirate GPI, use multi-step estimation (M-step Bellman equation) at the approximate policy evaluation step for estimating the value function and its gradient called costate, respectively. Then, we show that these two methods with the same update horizon can be considered equivalent in the iteration domain. Furthermore, monotonically increasing and decreasing convergences, so called value iteration (VI)-mode and policy iteration (PI)-mode convergences, are proved to hold for the proposed multirate GPIs. Further, general convergence properties in terms of eigenvalues are also studied. The data-driven online implementation methods for the proposed HDP and DHP are demonstrated and finally, we present the results of numerical simulations performed to verify the effectiveness of the proposed methods.

关键词： Multirate generalised policy iteration heuristic dynamic programming dual heuristic dynamic programming adaptive dynamic programming mixed-mode convergence linear quadratic regulation

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adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第1期63卷 33-40页

作者： Wang, Jin Fung, Richard Y. K. City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Hong Kong Peoples R China

Objectives: A well-developed appointment system can help increase the utilization of medical facilities in an outpatient department. This paper outlines the development of an appointment system that can make an outpatient department work more efficiently and improve patient satisfaction level. Methods: A Markov decision process model is proposed to schedule sequential appointments with the consideration of patient preferences in order to maximize the patient satisfaction level. adaptive dynamic programming algorithms are developed to avoid the curse of dimensionality. These algorithms can dynamically capture patient preferences, update the value of being a state, and thus improve the appointment decisions. Results: Experiments were conducted to investigate the performance of the algorithms. The convergence behaviors under different settings, including the number of iterations needed for convergence and the accuracy of results, were examined. Bias-adjusted Kalman filter step-sizes were found to lead to the best convergence behavior, which stabilized within 5000 iterations. As for the effects of exploration and exploitation, it resulted in the best convergence behavior when the probability of taking a myopically optimal action equaled 0.9. The performance of value function approximation algorithm was greatly affected by the combination of basis functions. Under different combinations, errors varied from 2.7% to 8.3%. More preferences resulted in faster convergence, but required longer computation time. Conclusions: System parameters are adaptively updated as bookings are confirmed. The proposed appointment scheduling system could certainly contribute to better patient satisfaction level during the booking periods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Markov decision process adaptive dynamic programming Outpatient department Appointment scheduling Patient preferences

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adaptive dynamic programming-based fault-tolerant attitude control for flexible spacecraft with limited wireless resources

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Science China(Information Sciences) 2023年第10期66卷 213-228页

作者： Ming LIU Qiuhong LIU Lixian ZHANG Guangren DUAN Xibin CAO School of Astronautics Harbin Institute of Technology Beijing Institute of Control Engineering

This paper investigates the attitude control for flexible spacecraft subject to actuator faults and limited onboard communication resources. The control torque quantization scheme is considered between the controller and actuator to reduce the communication burden on the spacecraft. An integral sliding mode control method is designed to stabilize the closed-loop attitude control system and ensure the near-optimal performance of the sliding motion. First, an iterative learning observer scheme is employed to reconstruct the actuator faults and the unknown nonlinear flexible dynamics. Second, an integral sliding surface is combined with the backstepping control method to resolve the time-varying characterization of the closed system. Third, a combination of the adaptive dynamic programming technique and the adaptive single critic neural network approximation is employed to examine the optimal control policy. Finally, the efficacy of the proposed spacecraft attitude control method is demonstrated via a simulation.

关键词： flexible spacecraft fault-tolerant attitude control signal quantization adaptive dynamic programming neural network learning

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adaptive dynamic programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems with ε-Error Bound

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第1期22卷 24-36页

作者： Wang, Fei-Yue Jin, Ning Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an epsilon-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming learning control neural control neural dynamic programming optimal control reinforcement learning

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adaptive dynamic programming and Optimal Control of Unknown Multiplayer Systems Based on Game Theory

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IEEE ACCESS 2022年 10卷 77695-77706页

作者： Zhao, Jingang Weifang Univ Sch Machinery & Automat Weifang 261061 Shandong Peoples R China Weifang Univ Inst Intelligent Percept & Control Complex Syst Weifang 261061 Shandong Peoples R China

In this paper, we present a new adaptive dynamic programming (ADP) scheme to solve the optimal control problem of multi-player systems with unknown dynamics from the perspective of nonzero-sum (NZS) games. In the presented scheme, a new iterative equation is given. On the basis of the given iterative equation, the control policy and corresponding value function for each player can be learned by using the state and input data, which does not need to identify the system dynamics. To overcome the difficulty of unknown system dynamics, neural network (NN)-based function approximation techniques are employed in the implementation. Based on the given iterative equation and NN-based function approximation techniques, a new non-model-based ADP algorithm is developed. The convergence of the developed non-model-based ADP algorithm is rigorously analyzed and proved. Finally, two numerical simulation examples are provided to demonstrate the performance of the developed non-model-based ADP algorithm.

关键词： Games Mathematical models Heuristic algorithms Approximation algorithms System dynamics Vehicle dynamics Optimal control adaptive dynamic programming nonzero-sum (NZS) games multi-player systems coupled Hamilton-Jacobi (HJ) equations neural network (NN)

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adaptive dynamic programming based robust control of nonlinear systems with unmatched uncertainties

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NEUROCOMPUTING 2020年 395卷 56-65页

作者： Zhao, Jun Na, Jing Gao, Guanbin Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

This paper proposes a new approach to address robust control design for nonlinear continuous-time systems with unmatched uncertainties. First, we transform the robust control problem into an equivalent optimal control problem, which allows to simplify the control design. A critic neural network (NN) is then adopted to reformulate the derived Hamilton-Jacobi-Bellman (HJB) equation based on the optimal control methodology. Then, a novel adaptation algorithm is used to online directly estimate the unknown NN weights, so as to achieve the guaranteed convergence. The control system stability and the convergence of the derived control action to the optimal solution can be rigorously proved. Finally, two simulation examples are provided to illustrate the validity and efficacy of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Robust control Neural network adaptive control Uncertain systems

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adaptive dynamic programming-based decentralized event-triggered control of large-scale nonlinear systems

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ASIAN JOURNAL OF CONTROL 2022年第4期24卷 1542-1556页

作者： Hu, Chuanhao Zou, Yuanyuan Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

This paper proposes an adaptive dynamic programming (ADP)-based decentralized event-triggered control strategy for large-scale nonlinear systems with event-triggered scheme to efficiently reduce communication cost and computational burden. Under the event-triggered mechanism, an local neural networks (NNs)-based observer is introduced to identify the mismatched interconnections and the estimation error is guaranteed to be uniformly ultimately bounded (UUB). Then, a decentralized triggering condition related to the approximation error of interconnections is designed to reduce the local controller updates with guaranteed overall stabilization of large-scale systems. By virtue of critic-only structure, the local optimal control policy can be approximated via aperiodic tuning rule using ADP. In addition, the closed-loop large-scale system ensures to be asymptotically stable with adaptive triggering threshold according to Lyapunov method. Finally, the simulation results justify the theoretical analysis and illustrate the effectiveness of the proposed event-triggered control (ETC) strategy.

关键词： adaptive dynamic programming critic NNs event-triggered NN-based observer

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