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检索条件"主题词=Adaptive Dynamic Programming"

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Safe Off-policy Reinforcement Learning Using Barrier Functions

Safe Off-policy Reinforcement Learning Using Barrier Functio...

引用

American Control Conference (ACC)

作者： Marvi, Zahra Kiumarsi, Bahare Michigan State Univ Dept Elect & Comp Engn 428 S Shaw LaneEngn Bldg E Lansing MI 48824 USA

ISBN: (纸本)9781538682661

This paper presents a safe off-policy reinforcement learning (RL) scheme to design optimal controllers for systems with uncertain dynamics. The utility function for which its optimization achieves a desired behavior is augmented with a control barrier function (CBF) candidate providing a platform for merging safety planning and optimal control design. A damping factor is included in the CBF providing a design tool to specify the relative importance of performance and safety. As one of the main contributions of this paper, it is shown that by iterative approximation of the value function, the safety properties of CBF are certified which bridges the broad capability of barrier functions into the learning-based approaches. Then, the safety of control system is proved accordingly. Stability and optimality of the control system in a safe condition are verified. Afterward, an off-policy RL algorithm is used to obtain the safe and optimal controller without requiring full knowledge about the system dynamics. The efficiency of the proposed method is demonstrated on the lane changing as an automotive control problem.

关键词： actor/critic control barrier function safety reinforcement learning adaptive dynamic programming

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Model-Free Optimal Consensus Control for Multi-Agent Systems Using Kernel-Based ADP Method

Model-Free Optimal Consensus Control for Multi-Agent Systems...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, Wei Chen, Xin Chen, Luefeng Wu, Min China Univ Geosci Sch Automat Wuhan Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Hubei Peoples R China

ISBN: (纸本)9781538616451

adaptive dynamic programming (ADP) is a prevalent way to solve the coupled Hamilton-Jacobi-Bellman (HJB) equations of the optimal consensus control for multi-agent systems (MAS). Neural networks (NNs) are normally used to approximate the value functions in ADP. However, NNs with manually designed features may influence the approximation ability. In this study, kernel-based methods which do not need to set the value function model structure in advance are adopted for value functions approximation. Moreover, to overcome the deficiency that most of the system dynamics are unknown, or the system is too complex to obtain the accurate dynamics. Local action value functions are defined, and kernel-based methods are used to approximate the local action value functions. Thus, an action dependent heuristic dynamic programming (ADHDP) approach using kernel-based local action value functions approximation is developed to achieve the optimal consensus control model-freely. The developed approach uses historical sample data to learn the system dynamics, and avoids the traditional system identification scheme. Simulation results are provided to demonstrate the effectiveness of the presented approach.

关键词： Multi-agent system adaptive dynamic programming optimal consensus control kernel method model-free

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dynamic Event-Driven Decentralized Tracking Control of Input-Saturated Systems 43

Dynamic Event-Driven Decentralized Tracking Control of Input...

引用

43rd Chinese Control Conference (CCC)

作者： Zheng, Wenqian Meng, Jianling Yang, Xiong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper studies the decentralized tracking control (DTC) problem of asymmetrically input-saturated systems through a dynamic event-driven (ED) approach. First, the DTC problem is converted into the decentralized regulation problem of augmented systems. Then, it is further changed into a series of unsaturated optimal control problems of nominal subsystems with novel value functions. Meanwhile, the dynamic ED mechanisms and ED Hamilton-Jacobi-Bellman equations (ED-HJBEs) are presented. To solve the ED-HJBEs, only critic neural networks (NNs) are constructed with their weights updated in the adaptive dynamic programming framework. After that, the Lyapunov approach is applied to prove the uniform ultimate boundedness of tracking errors and critic NNs' weight estimation errors. Finally, simulations are provided to validate the proposed decentralized control scheme.

关键词： adaptive dynamic programming Asymmetric input saturation Decentralized tracking control Event-driven control

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adaptive Critic H∞ Control of Nonlinear Systems With Disturbance and Input Constraints 13

Adaptive Critic <i>H</i>∞ Control of Nonlinear Systems With...

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13th World Congress on Intelligent Control and Automation (WCICA)

作者： Wan, Guishan Wang, Ding Liu, Derong Hou, Jiaxu Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538673454

For a class of discrete-time nonlinear systems with known internal dynamics, one of the problems worth discussing is H-infinity control, which could be used to attenuate the influence of disturbance effectively. To deal with the H-infinity control problem for nonlinear systems with disturbance and input constraints, we need to get the solution of the corresponding Hamilton-JacobiIsaacs (HJI) equation that is difficult to solve by mathematical analysis. This paper provides an online adaptive critic learning algorithm to obtain the approximate Nash equilibrium solutions of the two-player zero-sum game, which has the close connection with H-infinity control. Meanwhile, a non-quadratic performance functional is introduced to handle the constrained problem. In order to implement the algorithm, a framework based on actor-criticdisturbance structure is designed and three neural networks are constructed. Finally, via simulation study, the applicability of the proposed scheme is verified.

关键词： Nonlinear system adaptive dynamic programming input constraints H-infinity Control neural network Nash equilibrium solution

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adaptive Critic Designs of Optimal Control for Ice Storage Air Conditioning Systems 25th

Adaptive Critic Designs of Optimal Control for Ice Storage A...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Liao, Zehua Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9783030042394;9783030042387

In this paper, the optimal control scheme for ice storage air conditioning system is solved via an adaptive critic design method. adaptive critic design is also called adaptive dynamic programming (ADP). First, the operation of the air conditioning system is analyzed. Next, adaptive critic method is designed to realize the optimal control for the air conditioning system. Numerical results show that using the databased ADP optimal control method can reduce the operation costs.

关键词： Ice storage air conditioning adaptive critic design adaptive dynamic programming Neural network Optimal control

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Finite-horizon optimal control for affine nonlinear systems with dead-zone control input

Finite-horizon optimal control for affine nonlinear systems ...

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Chinese Automation Congress (CAC)

作者： Lin, Xiaofeng Ding, Qiang Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

ISBN: (纸本)9781479903337

For affine nonlinear systems with dead-zone control input and discount factor in performance index function, a finite-horizon adaptive dynamic programming (ADP) algorithm is proposed in this paper. To deal with dead-zone nonlinearity, a new utility function is defined, and the corresponding discrete-time Hamilton-Jacobi-Bellman (DTHJB) equation is derived. Then, finite-horizon ADP algorithm is developed to obtain the optimal control with convergence analysis. A dead-zone network is introduced for implementing the algorithm, which is different from implementing the traditional ADP algorithm. A simulation example is given to demonstrate the effectiveness of the presented algorithm.

关键词： adaptive dynamic programming finite-horizon dead-zone optimal control

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Applying Expectation-Maximization Evaluation on Approximate Optimal Control 12

Applying Expectation-Maximization Evaluation on Approximate ...

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12th Annual IEEE International Systems Conference (SYSCON)

作者： Zhang, Songtao Dubay, Rickey Univ New Brunswick Dept Mech Engn Fredericton NB Canada

ISBN: (纸本)9781538636640

In this paper we proposed an approach of approximating optimal tracking via expectation-maximization (EM) evaluation. From the discussion of applying reinforcement learning (RL) for a system with unknown internal dynamics, we present the challenge of using a classical frame of Q-learning on a tracking task. Further we explained the idea of redefining the cost function (i.e. criterion) of Q-learning to satisfy the requirement for the system dynamic knowledge for the tracking task. We explained the advantages of dividing the original trajectory tracking task into two machine learning subtasks (i.e. learning the quadratic regulator and learning the baseline command generator) on-line. Details are given on the integration of the Q-learning frame and EM algorithm as well as the convergence to the optimum control via iterative estimation of an optimal regulator and a baseline generator. Initial simulation results of this approach using a second order system showed the ability of the Q-learning frame integrated with the EM algorithm approximates to the optimal tracking task.

关键词： Q-learning quadratic control expectation-maximization algorithm adaptive dynamic programming

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Stackelberg Differential Game-based Optimal Fault Estimation and Accommodation for Continuous-time Linear Systems 21st

Stackelberg Differential Game-based Optimal Fault Estimation...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Xu, Yuhang Yang, Hao Jiang, Bin Polycarpou, Marios M. Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-1678 Nicosia Cyprus Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

This paper deals with the optimal fault estimation and accommodation problem for a class of linear systems in the framework of Stackelberg differential game theory. In this framework, the observer plays the role of the follower, while the system plays the role of the leader in making sequential decisions. A dual controller approach is used to design an auxiliary controller for the observer such that it can non-cooperate with the controller of the system to achieve the Stackelberg equilibrium. To achieve the online updating of the fault-tolerant controller, an adaptive dynamic programming methodology is used by establishing two critic neural networks for the observer and system respectively. Finally, a simulation is presented to illustrate the efficiency and applicability of the theoretical results. Copyright (C) 2020 The Authors.

关键词： Optimal fault estimation optimal fault-tolerant control Stackelberg differential game adaptive dynamic programming

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Structured Online Learning-based Control of Continuous-time Nonlinear Systems 21st

Structured Online Learning-based Control of Continuous-time ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Farsi, Milad Liu, Jun Univ Waterloo Appl Math Dept Waterloo ON Canada

Model-based reinforcement learning techniques accelerate the learning task by employing a transition model to make predictions. In this paper, a model-based learning approach is presented that iteratively computes the optimal value function based on the most recent update of the model. Assuming a structured continuous-time model of the system in terms of a set of bases, we formulate an infinite horizon optimal control problem addressing a given control objective. The structure of the system along with a value function parameterized in the quadratic form provides a flexibility in analytically calculating an update rule for the parameters. Hence, a matrix differential equation of the parameters is obtained, where the solution is used to characterize the optimal feedback control in terms of the bases, at any time step. Moreover, the quadratic form of the value function suggests a compact way of updating the parameters that considerably decreases the computational complexity. Considering the state-dependency of the differential equation, we exploit the obtained framework as an online learning-based algorithm. In the numerical results, the presented algorithm is implemented on four nonlinear benchmark examples, where the regulation problem is successfully solved while an identified model of the system is obtained with a bounded prediction error. Copyright (C) 2020 The Authors.

关键词： Reinforcement learning Model-based learning Optimal control Feedback control Continuous-time control adaptive dynamic programming Sparse identification

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Neural-Network-Based Self-Learning Disturbance Rejection Design for Continuous-Time Nonlinear Constrained Systems 40

Neural-Network-Based Self-Learning Disturbance Rejection Des...

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40th Chinese Control Conference (CCC)

作者： Li, Menghua Wang, Ding Qiao, Junfei Xu, Xin 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 Inst Artificial Intelligence Beijing 100124 Peoples R China

ISBN: (纸本)9789881563804

In this paper, we adopt a neural-network-based adaptive dynamic programming (ADP) method to solve the disturbance rejection problem for continuous-time nonlinear systems with control constraints. First, we define a suitable function and then derive the optimal control law with control constraints and the worst disturbance law. Besides, the constrained Hamilton-Jacobi-Isaacs equation for continuous-time nonlinear constrained systems is derived. Then, only one critic neural network is used to approximate the optimal cost function. Consequently, the approximate optimal control law and the approximate worst disturbance law are obtained. Additionally, a new updating rule is developed in the process of neural critic learning. Finally, the simulation results show that the self-learning optimal control is realized and the effectiveness of the disturbance rejection is verified by using the neural-network-based ADP method.

关键词： Optimal control Nonlinear constrained systems adaptive dynamic programming Neural networks Self-learning disturbance rejection H-infinity control

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