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检索条件"主题词=Adaptive Dynamic Programming"

共 1118 条记录，以下是941-950 订阅

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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 ...

引用

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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Value Iteration Based Continuous-time Nonlinear Constrained Optimal Tracking Controller Design

Value Iteration Based Continuous-time Nonlinear Constrained ...

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Chinese Automation Congress (CAC)

作者： Xiao, Geyang Zhou, Boyang Lou, Kaiyi Chen, Zhengrong Zhejiang Lab Res Ctr Ind Internet Hangzhou Peoples R China

ISBN: (纸本)9781728176871

The value iteration mechanism is introduced to design the constrained optimal tracking controller for continuous-time nonlinear system in this paper. Unlike most studies were developed by policy iteration, the proposed VI algorithm is designed to be started with arbitrary semi-definite positive function, and thus no restriction is in the initial condition. The iterative property of the proposed algorithm is discussed and the optimality of the iterated results are obtained. To verify the effectiveness, a nonlinear case is simulated.

关键词： Constrained control adaptive dynamic programming value iteration

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Optimal Control for Multi-agent Systems Using Off-Policy Reinforcement Learning 4

Optimal Control for Multi-agent Systems Using Off-Policy Rei...

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4th International Conference on Control and Robotics (ICCR)

作者： Wang, Hao Chen, Zhiru Wang, Jun Lu, Lijun Li, Mingzhe Henan XJ Metering Co Ltd Xuchang Peoples R China State Grid Shandong Elect Power Co Mkt Serv Ctr Metering Ctr Jinan Peoples R China

ISBN: (纸本)9781665486415

To achieve the consensus for discrete-time multi-agent systems, an optimal control policy is designed based on off-policy reinforcement learning. By utilizing centralized learning and decentralized execution, we first define a centralized and shared value function. Then, a value iteration adaptive dynamic programming method is proposed to approach the solution of the Bellman optimality equation with convergence analysis. Furthermore, the actor-critic structure is given for the implementation purpose, where one single-critic network is given to approach the optimal centralized value function, and multi-actor networks are decentralized based on the local observation from the neighbors to obtain the optimal policy for each agent. Finally, the proposed algorithm is verified in a leader-follower consensus case.

关键词： Reinforcement learning multi-agent systems adaptive dynamic programming

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adaptive-Critic-Based Robust Control with Momentum Gradient Descent for Continuous-Time Nonlinear Systems 43

Adaptive-Critic-Based Robust Control with Momentum Gradient ...

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43rd Chinese Control Conference (CCC)

作者： Hu, Qinna Wang, Ding Liu, Ao Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Comput Intelligence & Intellige 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

ISBN: (纸本)9798350366907;9789887581581

In this paper, robust control problems are investigated for nonlinear continuous-time systems. A momentum-based gradient descent (GD) approach is developed to enhance the convergence performance of parameters in adaptive dynamic programming (ADP). By introducing the idea of momentum, the oscillation in the process of GD is alleviated and the selection of the learning rate becomes more flexible. Under the framework of ADP, the robust control problem is transformed into the optimal control problem by modifying the cost function. To avoid limitations of the initial admissible condition, an additional term is employed in the computation of the current gradient. Based on the online policy iteration algorithm, the momentum-based GD approach is constructed as an improved learning algorithm to optimize the critic network weights. Finally, a simulation is conducted to verify the effectiveness of the established learning strategy.

关键词： adaptive dynamic programming Momentum-based gradient descent Online policy iteration Optimal control Robust control

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Cooperative adaptive optimal output regulation of multi-agent systems with an application to connected and autonomous vehicles 7

Cooperative adaptive optimal output regulation of multi-agen...

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IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Dong, Yuchen Gao, Weinan Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

ISBN: (纸本)9798350363029;9798350363012

This paper proposes a novel control approach based on gradient descent methods to solve the problem of cooperative adaptive optimal output regulation of continuous-time linear multi-agent systems. This proposed approach calculates gradients through online data rather than model information, such that a data-driven distributed adaptive controller is developed by adaptive dynamic programming, which ensures that each follower can achieve asymptotic tracking and disturbance rejection. Finally, the effectiveness of the proposed control algorithm is verified by simulation of connected and autonomous vehicles.

关键词： Cooperative output regulation adaptive dynamic programming gradient descent method connected and autonomous vehicles

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Safe Off-policy Reinforcement Learning Using Barrier Functions

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

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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...

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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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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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