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Guaranteed cost neural tracking control for a class of uncertain nonlinear systems using adaptive dynamic programming

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NEUROCOMPUTING 2016年 198卷 80-90页

作者： Yang, Xiong Liu, Derong Wei, Qinglai Wang, Ding Chinese Acad Sci Complex Syst Inst Automat State Key Lab Management & Control Beijing 100190 Peoples R China Univ Sci & Technol Sch Automat & Elect Engn Beijing 100083 Peoples R China

This paper presents an adaptive dynamic programming-based guaranteed cost neural tracking control algorithm for a class of continuous-time matched uncertain nonlinear systems. By introducing an augmented system and employing a modified cost function with a discount factor, the guaranteed cost tracking control problem is transformed into an optimal tracking control problem. Unlike existing optimal tracking control algorithms often requiring the control matrix to be invertible, the developed control algorithm relaxes this restrictive condition under the assumption that the system is controllable. A single critic neural network (NN) is constructed to approximate the solution of the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal augmented error dynamics. Utilizing the newly developed critic NN, the optimal tracking control can be derived without policy iteration. All signals in the closed-loop system are proved to be uniformly ultimately bounded via Lyapunov's direct method. In addition, the developed control scheme is verified to guarantee that the tracking errors converge to an adjustable neighborhood of the origin. Two numerical examples are provided to illustrate the effectiveness and applicability of the developed approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Guaranteed cost control Hamilton-Jacobi-Bellman equation Neural network Nonlinear system reinforcement learning

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A Lyapunov function based optimal hybrid power system controller for improved transient stability

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ELECTRIC POWER SYSTEMS RESEARCH 2016年 137卷 6-15页

作者： Yousefian, R. Kamalasadan, S. Univ N Carolina Dept Elect & Comp Engn Charlotte NC 28223 USA

In this paper, an intelligent power system stabilizer based on a stable and optimal hybrid learning-based adaptive control architecture is proposed which is evolved from approximate dynamic programming technique. The hybrid learning controller is based on two algorithms: (a) A reinforcement learning neural network controller implemented by adaptive critic design (ACD) and (b) A model reference adaptive controller (MRAC). The proposed method uses a novel value priority architecture for integrating these two algorithms. The designed ACD approximates nonlinear functional dynamics of the power system and interacts with the MRAC that adapts based on parametric changes. The value priority scheme developed based on a softmax network generates a hybrid control signal derived from a Lyapunov stability function that evaluates identification, performance, and stability of each controller. The overall hybrid control algorithm ensures convergence to an optimal control solution without using an explicit model of the system and at the same time ensures overall system stability. Theoretical results are validated by simulation studies for electric-generator stabilization on a 2-area 5-generator power system and as a wide-area controller on ieee 68-bus system. (C) 2016 Elsevier B.V. All rights reserved.

关键词： adaptive control adaptive critic design Power system stabilizer reinforcement learning Supervised learning

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GrHDP Solution for Optimal Consensus Control of Multiagent Discrete-Time Systems

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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第7期50卷 2362-2374页

作者： Zhong, Xiangnan He, Haibo Univ North Texas Dept Elect Engn Denton TX 76207 USA Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

This paper develops a new online learning consensus control scheme for multiagent discrete-time systems by goal representation heuristic dynamic programming (GrHDP) techniques. The agents in the whole system are interacted with each other through a communication graph structure. Therefore, each agent can only receive the information from itself and its neighbors. Our goal is to design the GrHDP method to achieve consensus control which makes all the agents track the desired dynamics and simultaneously makes the performance indices reach Nash equilibrium. The new local internal reinforcement signals and local performance indices are provided for each agent and the corresponding distributed control laws are designed. Then, GrHDP algorithm is developed to solve the multiagent consensus control problem with the proof of convergence. It is shown that the designed local internal reinforcement signals are bounded signals and the local performance indices can monotonically converge to their optimal values. Moreover, the desired distributed control laws can also achieve optimal. Two simulation studies, including one with four agents and another with ten agents, are applied to validate the theoretical analysis and also demonstrate the effectiveness of the proposed method.

关键词： Multi-agent systems Synchronization Discrete-time systems Nash equilibrium Decentralized control Neural networks Performance analysis adaptive dynamic programming (ADP) consensus control goal representation multiagent systems online learning neural networks

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learning-Based Attitude Tracking Control With High-Performance Parameter Estimation

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ieee TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 2218-2230页

作者： Dong, Hongyang Zhao, Xiaowei Hu, Qinglei Yang, Haoyang Qi, Pengyuan Univ Warwick Intelligent Control & Smart Energy Res Grp Sch Engn Coventry CV4 7AL W Midlands England Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China

This article aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement-learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within predetermined bounds. Based on it, a critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-the-loop experimental tests.

关键词： Attitude control Uncertainty Task analysis Optimal control Cost function Tracking Mathematical models adaptive control adaptive dynamic programming (ADP) attitude tracking control parameter estimation

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adaptive reinforcement learning Strategy-Based Sliding Mode Control of Uncertain Euler-Lagrange Systems With Prescribed Performance Guarantees: Autonomous Underwater Vehicles-Based Verification

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ieee TRANSACTIONS ON FUZZY SYSTEMS 2024年第11期32卷 6160-6171页

作者： Wu, Yang Wang, Yue-Ying Xie, Xiang-Peng Wu, Zheng-Guang Yan, Huai-Cheng Qilu Univ Technol Shandong Acad Sci Inst Automat Jinan 250014 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

This article studies the tracking control problem of uncertain Euler-Lagrange systems. Despite receiving widespread attention in recent years, the problem remains unresolved to a large content when considering response quality, optimality, robustness, and conservatism. The main challenge lies in how to integrate performance constraints into adaptive dynamic programming (ADP) algorithm and achieve a balance between robustness and conservatism within this framework. To that end, this study proposes a new performance constraint-handling sliding mode manifold, a new prescribed performance function, and a new ADP-oriented observer disturbance observer. The above theoretical findings, together with the fuzzy logic system-based ADP algorithm, realize the convergence of tracking errors to a prespecified residual set within a finite-time setting in an optimal manner, enhance robustness, and reduce conservatism. The proposed controller facilitates the practical application of tracking control for Euler-Lagrange systems. Simulations on an autonomous underwater vehicle demonstrate the effectiveness and benefits of the proposed method.

关键词： adaptive dynamic programming (ADP) Euler-Lagrange (EL) system fuzzy logic system (FLS) prescribed performance control (PPF) sliding mode control

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Policy Gradient Approaches for Multi-Objective Sequential Decision Making: A Comparison

Policy Gradient Approaches for Multi-Objective Sequential De...

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ieee symposium on adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Parisi, Simone Pirotta, Matteo Smacchia, Nicola Bascetta, Luca Restelli, Marcello Politecn Milan Dept Elect Informat & Bioengn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9781479945528

This paper investigates the use of policy gradient techniques to approximate the Pareto frontier in Multi-Objective Markov Decision Processes (MOMDPs). Despite the popularity of policy-gradient algorithms and the fact that gradient-ascent algorithms have been already proposed to numerically solve multi-objective optimization problems, especially in combination with multi-objective evolutionary algorithms, so far little attention has been paid to the use of gradient information to face multi-objective sequential decision problems. Three different Multi-Objective reinforcement-learning (MORL) approaches are here presented. The first two, called radial and Pareto following, start from an initial policy and perform gradient-based policy-search procedures aimed at finding a set of non-dominated policies. Differently, the third approach performs a single gradient-ascent run that, at each step, generates an improved continuous approximation of the Pareto frontier. The parameters of a function that defines a manifold in the policy parameter space are updated following the gradient of some performance criterion so that the sequence of candidate solutions gets as close as possible to the Pareto front. Besides reviewing the three different approaches and discussing their main properties, we empirically compare them with other MORL algorithms on two interesting MOMDPs.

关键词： Pareto optimisation approximation theory decision making evolutionary computation gradient methods learning (artificial intelligence) MOMDPs MORL approaches Pareto following Pareto frontier approximation gradient-ascent algorithms gradient-based policy-search procedures multiobjective Markov decision processes multiobjective evolutionary algorithms multiobjective optimization problems multiobjective reinforcement-learning approaches multiobjective sequential decision making nondominated policies performance criterion policy gradient approaches policy-gradient algorithms radial following Algorithm design and analysis Approximation algorithms Approximation methods Manifolds Measurement Optimization Water resources evolutionary algorithm Performance metrics Pareto optimisation Algorithm design and analysis Manifolds Approximation method gradient methods Approximation Theory Approximation algorithms Water Resources Policies decision making

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Manifold-Based reinforcement learning via Locally Linear Reconstruction

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2017年第4期28卷 934-947页

作者： Xu, Xin Huang, Zhenhua Zuo, Lei He, Haibo Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

Feature representation is critical not only for pattern recognition tasks but also for reinforcement learning (RL) methods to solve learning control problems under uncertainties. In this paper, a manifold-based RL approach using the principle of locally linear reconstruction (LLR) is proposed for Markov decision processes with large or continuous state spaces. In the proposed approach, an LLR-based feature learning scheme is developed for value function approximation in RL, where a set of smooth feature vectors is generated by preserving the local approximation properties of neighboring points in the original state space. By using the proposed feature learning scheme, an LLR-based approximate policy iteration (API) algorithm is designed for learning control problems with large or continuous state spaces. The relationship between the value approximation error of a new data point and the estimated values of its nearest neighbors is analyzed. In order to compare different feature representation and learning approaches for RL, a comprehensive simulation and experimental study was conducted on three benchmark learning control problems. It is illustrated that under a wide range of parameter settings, the LLR-based API algorithm can obtain better learning control performance than the previous API methods with different feature representation schemes.

关键词： adaptive dynamic programming learning control manifold learning Markov decision processes (MDPs) reinforcement learning (RL) value function approximation (VFA)

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dynamic Event-Triggered Control for Hierarchical Differential Games

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年

作者： Xue, Shan Luo, Biao Zhang, Weidong Liu, Derong Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

This paper proposes a novel dynamic event-triggered control method for a class of completely unknown nonaffine hierarchical differential games, incorporating asymmetric boundaries in both system states and control strategies. To tackle this problem, dynamic feedback and mapping functions are first introduced to construct an unconstrained affine augmented system. Then, integral reinforcement learning techniques are used to derive the Hamilton-Jacobi equation without the original system dynamics. Furthermore, dynamic event-triggered control is employed to alleviate the network transmission burden. During the algorithm implementation, critic neural networks are designed for each agent. Analysis results show that the states and weights are ultimately uniformly bounded. Finally, simulation results using the torsional pendulum system and RLC circuit system validate the effectiveness of the present method.

关键词： Differential games Games Optimal control Event detection dynamic programming Automation Stability analysis Optimization Nonlinear dynamical systems Mathematical models adaptive dynamic programming integral reinforcement learning event-triggered control differential game neural network

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Opposition-based reinforcement learning in the management of water resources

Opposition-based reinforcement learning in the management of...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Mahootchi, M. Tizhoosh, H. R. Ponnambalam, K. Univ Waterloo Sysr Design Engn 200 Univ Ave W Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424407064

Opposition-Based learning (OBL) is a new scheme in machine intelligence. In this paper, an OBL version Q-learning which exploits opposite quantities to accelerate the learning is used for management of single reservoir operations. In this method, an agent takes an action, receives reward, and updates its knowledge in terms of action-value functions. Furthermore, the transition function which is the balance equation in the optimization model determines the next state and updates the action-value function pertinent to opposite action. Two type of opposite actions will be defined. It will be demonstrated that using OBL can significantly improve the efficiency of the operating policy within limited iterations. It is also shown that this technique is more robust than Q-learning.

关键词： water reservoirs Q-learning opposite action reinforcement learning

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An adaptive Hierarchical Energy Management Strategy for Hybrid Electric Vehicles Combining Heuristic Domain Knowledge and Data-Driven Deep reinforcement learning

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ieee TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2022年第3期8卷 3275-3288页

作者： Hu, Bo Li, Jiaxi Chongqing Univ Technol Key Lab Adv Mfg Technol Automobile Parts Minist Educ Chongqing 400054 Peoples R China Ningbo Yinzhou DLT Technol Co Ltd Ningbo 315000 Peoples R China

With the development of artificial intelligence, there has been a growing interest in machine learning-based control strategy, among which reinforcement learning (RL) has opened up a new direction in the field of hybrid electric vehicle (HEV) energy management. However, the issues of the current RL setting ranging from inappropriate battery state-of-charge (SOC) constraint to ineffective and risky exploration make it inapplicable to many industrial energy management strategy (EMS) tasks. To address this, an adaptive hierarchical EMS combining heuristic equivalent consumption minimization strategy (ECMS) knowledge and deep deterministic policy gradient (DDPG), which is a state-of-the-art data-driven RL algorithm, is proposed in this work. For comparison purposes, the proposed strategy is contrasted with dynamic programming (DP), proportion integration differentiation (PID)-based adaptive ECMS, and rule-based and standard RL-based counterparts, and the results show that the fuel consumption after SOC correction for the proposed strategy is very close to that of the DP-based control and lower than that of the other three benchmark strategies. Considering that the proposed strategy can make better use of the RL techniques while realizing an effective, efficient, and safe exploration in a data-driven manner, it may become a strong foothold for future RL-based EMS to build on, especially when the controller has to be trained directly and from scratch in a real-world environment.

关键词： State of charge Energy management Hybrid electric vehicles Optimization Task analysis Environmental management Engines Energy management strategy (EMS) heuristic knowledge hierarchical hybrid electric vehicle (HEV) reinforcement learning (RL)

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