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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Velazquez, Alberto Xu, Lei Sardarmehni, Tohid Univ Texas Rio Grande Valley Edinburg TX USA Kent State Univ Kent OH 44242 USA Calif State Univ Northridge Northridge CA 91330 USA

ISBN: (纸本)9780791886670

In this paper, optimal tracking control is found for an input-affine nonlinear quadcopter using Single Network adaptive Critics (SNAC). The quadcopter dynamics consists of twelve states and four controls. The states are defined using two related reference frames: the earth frame, which describes the position and angles, and the body frame, which describes the linear and angular velocities. The quadcopter has six outputs and four controls, so it is an underactuated nonlinear system. The optimal control for the system is derived by solving a discrete-time recursive Hamilton-Jacobi-Bellman equation using a linear in-parameter neural network. The neural network is trained to find a mapping between a target costate vector and the current states. The network's weights are iteratively trained using the least-squares approximation method until the maximum number of iterations or convergence is reached, and training begins at the final time and proceeds backward to the initial time. The trained neural controller applies online optimal feedback control that tracks a trajectory, minimizes control effort, and satisfies the optimality condition. The SNAC method provides a controller that can handle all initial conditions within the domain of training and all times less than the training's final time.

关键词： Quadcopter Optimal Control adaptive dynamic programming Reinforcement Learning

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Optimal Event-Triggered Impulsive Control for Stochastic Systems using ADP 7

Optimal Event-Triggered Impulsive Control for Stochastic Sys...

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7th International Conference on Automation, Control and Robotics Engineering (CACRE)

作者： Liang, Mingming Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (数字)9781665403870

ISBN: (纸本)9781665403870

In this paper, a novel general-event-based impulsive transition matrix (GITM) is constructed, which can reveal the probability distribution evolution patterns for all system states across the impulsive instants, instead of the regular time indexes. Based on the GITM, the event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and a high-efficiency event-triggered impulsive adaptive dynamic programming (HEIADP) algorithm are developed. It is shown that the proposed methods can solve the optimal impulsive control problems of discrete stochastic systems, while reducing the computational and communication burden caused by updating the controller periodically. Convergency analysis is provided to show that both the ETIADP and HEIADP algorithms can converge to the optimal impulsive performance index function. The proposed HEIADP algorithm updates policies and value functions partially at each iteration step according to actual hardware constraints, reducing the memory consumption of the ADP implementation. The simulation experiment is conducted to validate the effectiveness of the developed methods.

关键词： adaptive dynamic programming impulsive stochastic systems event-triggering optimal control

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Online Value Iteration for Discrete-Time Nonlinear Optimal Regulation with Stability Guarantee 4

Online Value Iteration for Discrete-Time Nonlinear Optimal R...

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4th International Conference on Control and Robotics (ICCR)

作者： Wang, Yuan Wang, Ding Wu, Junlong Zhao, Mingming Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

ISBN: (纸本)9781665486415

In this paper, the intelligent and online value iteration (VI) algorithms are developed to solve the optimal control problem for nonlinear discrete-time systems. First, the intelligent VI algorithm combines the advantages of traditional VI initialized by the zero cost function and stabilizing VI initialized by the admissible control policy. The traditional VI is easy to implement and can provide the initial admissible control policy for the stabilizing VI. Meanwhile, stabilizing VI can guarantee all control policies are admissible. Second, based on the concept of the attraction domain, an online value iteration algorithm is proposed to regulate the closed-loop system by using immature control policies rather than the fixed optimal control policy. It ensures that the state trajectory converges to the origin of the attraction domain. Finally, simulations are carried out and the results show the effectiveness of the two new VI algorithms.

关键词： adaptive dynamic programming asymptotic stability attraction domain online value iteration

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Event-Triggered Robust Control for Fully Cooperative Games of Uncertain Nonlinear Multi-player Systems 19

Event-Triggered Robust Control for Fully Cooperative Games o...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhang, Shunchao Liu, Derong Zhang, Yongwei Zhao, Bo Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

This paper develops an adaptive dynamic programming-based event-triggered robust control method for fully cooperative games of nonlinear multi-player systems with uncertainties. By designing a modified value function, the robust control problem is transformed into a fully cooperative (FC) game of the nominal system. Then, a critic neural network is constructed to solve the Hamilton-Jacobi-Bellman equation of the FC game, and the optimal control laws of all players are obtained. Under the event-triggered mechanism, the designed event-triggering condition is employed to determine whether the control laws should be updated or not. The developed method saves computation and communication resources, since both the updating frequency of control laws and the communication between controllers and actuators are reduced. Furthermore, the closed-loop system is proven to be uniformly ultimately bounded thanks to the Lyapunov's direct method. A numerical example is employed to verify the effectiveness of the developed method.

关键词： adaptive dynamic programming Event-triggered control Fully cooperative games Uncertain multiplayer systems

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Cooperative Game-based Optimal Tracking Control for Modular Reconfigurable Robots via Generalized Fuzzy Hyperbolic Models 41

Cooperative Game-based Optimal Tracking Control for Modular ...

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第41届中国控制会议

作者： Xinye Zhu Tianjiao An Gang Wang Bo Dong School of Mechanical and Control Engineering Baicheng Normal University Department of Control Science and Engineering Changchun University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A cooperative game optimal tracking control via generalized fuzzy hyperbolic models for modular reconfigurable robots(MRR) system is *** to the NewtonEuler iteration method,dynamics model of robotic subsystem is established and the global state space equation is *** control inputs of n joints in the MRR system are taken as n participants of cooperative ***,the fusion error function containing position and velocity errors is defined to establish the cost *** improving robustness of the MRR,the generalized fuzzy hyperbolic is used to identify the unknown model *** coupled HJB equation is solved by critic neural network(NN) in the adaptive dynamic *** theory proves the trajectory tracking error of MRR system is uniformly ultimately bounded(UUB).Experimental results show the validity of the control method.

关键词： Index Terms—modular reconfigurable robots cooperative game adaptive dynamic programming generalized fuzzy hyperbolic models

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adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第2期66卷 888-894页

作者： Pang, Bo Jiang, Zhong-Ping NYU Tandon Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA

This article studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy adaptive dynamic programming (ADP) algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform convergence of the proposed algorithm to the optimal solutions are given for both the model-based and model-free cases. The VI-based ADP algorithm is able to find suboptimal controllers without assuming the knowledge of an initial stabilizing controller. Application to the optimal control of a triple inverted pendulum subjected to a periodically varying load demonstrates the feasibility and effectiveness of the proposed method.

关键词： adaptive dynamic programming linear periodic systems optimal control value iteration

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Zero-sum game-based neuro-optimal control of modular robot manipulators with uncertain disturbance using critic only policy iteration

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NEUROCOMPUTING 2021年 450卷 183-196页

作者： Dong, Bo An, Tianjiao Zhu, Xinye Li, Yuanchun Liu, Keping Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China

In this paper, a zero-sum differential game strategy-based neuro-optimal control method is presented via critic only policy iteration-adaptive dynamic programming (COPI-ADP) approach to address optimal trajectory tracking control problem of modular robot manipulators (MRMs) with uncertain disturbance. The dynamic model of modular robot manipulator systems is formulated as an integration of joint subsystems and unknown robotic model uncertainties are identified by the developed linear extension state observer. Then, the optimal control issue of the modular robot manipulator systems with uncertain disturbance is transformed into a two-player zero-sum differential game one. Based on adaptive dynamic programming and policy iteration algorithms, the Hamilton-Jacobi-Issacs (HJI) equation is approximately solved using only critic neural network and thus facilitating the feasible derivation of the approximated optimal control policy. The trajectory of tracking errors of modular robot manipulator system is guaranteed to be uniform ultimate bounded by using the Lyapunov theory. Finally, experiments are provided to demonstrate the advantage and effectiveness of the developed control method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Modular robot manipulators adaptive dynamic programming Critic only policy iteration Optimal control Neural network Zero-sum differential game

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Novel generalized policy iteration for efficient evolving control of nonlinear systems

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NEUROCOMPUTING 2024年 608卷

作者： Huang, Haiming Wang, Ding Wang, Hua Wu, Junlong Zhao, Mingming Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

In this article, we construct a novel generalized policy iteration framework to address optimal regulation problems for discrete-time nonlinear systems in a more efficient way. Relevant properties are investigated for the framework, including monotonicity and convergence of the iterative value function sequence as well as the admissibility of the iterative control policy. Additionally, an innovative approach is developed to seek an initial admissible control policy for the framework with an adjustable searching speed. Based on these, an evolving control algorithm is presented with stability guarantee. This algorithm employs iterative control policies for system control during the computation of the optimal control policy, as opposed to waiting for the generation of the optimal control policy before implementing control. Eventually, two simulation experiments are conducted with real-world physical backgrounds, in order to illustrate the performance of the proposed strategy.

关键词： adaptive critic designs adaptive dynamic programming Generalized policy iteration Evolving control Admissible control policy Optimal control

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Model-free adaptive optimal control for nonlinear multiplayer games with input disturbances

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NEUROCOMPUTING 2024年 580卷

作者： Shi, Jing Peng, Chen Zhang, Jin Zhang, Zhihao Xie, Xiangpeng Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Nanjing Univ Posts & Telecommun Inst Carbon Neutral Adv Technol Nanjing 210023 Peoples R China

In this paper, we investigate a model-free identifier-critic-based optimal adaptive controller for multiplayer games with the input disturbances. Specifically, we first adopt the identifier neural network to identify the system dynamics. Simultaneously, we use the critic neural network to estimate the optimal cost function thereby obtaining the estimated optimal controller. Further taking the input disturbances into consideration, we add a feedback gain into the estimated optimal controller so as to obtain the controller. The learning of the identifier and critic network is online and simultaneous. Then, we analyze the stability of the proposed approach. Eventually, the simulation results illustrate the validity of the proposed controller.

关键词： adaptive dynamic programming Multiplayer game Input disturbances adaptive control

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Guaranteed cost tracking control of constrained input nonlinear uncertain systems using event-based ADP

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NEUROCOMPUTING 2024年 572卷

作者： Dahal, Raju Kar, Indrani Indian Inst Technol Guwahati Dept Elect & Elect Engn Kamrup 781039 Assam India

This article investigates the guaranteed cost robust tracking control problem of nonlinear systems subjected to input constraint and unmatched uncertainty. The event -based adaptive dynamic programming (ADP) approach is utilized to address this problem. First, the tracking error and reference trajectory are combined to form an augmented uncertain system. Then, by decomposing the uncertainty into the matched and unmatched parts, the original tracking problem is converted into the optimal regulation problem of an auxiliary system. The cost function for the auxiliary system is defined, and the associated Hamilton-Jacobi-Bellman (HJB) equation is solved using a single critic neural network (NN). Moreover, a novel event -triggering rule is formulated, and it is shown that the designed event -based controller guarantees that the tracking error is uniformly ultimately bounded. The derivation of event -based guaranteed cost and its relation with the time -based counterpart is presented. The exclusion of the infamous Zeno behavior is guaranteed. The uniform ultimate boundedness of the critic weight estimation error is shown. Finally, the effectiveness of the proposed event -triggered ADP method is illustrated through simulations of the spring-mass-damper system and Van der Pol's oscillator with unmatched uncertainty.

关键词： Guaranteed cost tracking Constrained input Unmatched uncertainty Neural networks adaptive dynamic programming Event-triggered

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