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检索条件"主题词=Adaptive Dynamic Programming"

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Observer-based adaptive critic control for nonlinear continuous-time systems with actuator saturation 36

Observer-based adaptive critic control for nonlinear continu...

引用

第36届中国控制会议

作者： Tiechao Wang Huayang Zhang Huahao Chen College of Electrical Engineering Liaoning University of Technology

ISBN: (纸本)9781538629185

In this paper,an observer-based online optimal control scheme is presented for nonlinear continuous-time systems with actuator *** neural networks,which include an observer neural network,an critic neural network and an actuator neural network,comprise observer-based critic-actuator *** critic neural network is employed to approximate the cost *** control input depends on the the actuator neural network and the estimated system *** using Lyapunov stability theory,the system state estimate error,the neural network weight estimate errors are ensured to be uniformly ultimately bounded.A simulation example is given to demonstrate the validness of the proposed control method.

关键词： adaptive dynamic programming saturation constraint neural network state observer

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Optimal Control of Unknown Discrete-Time Nonlinear Systems with Constrained Inputs Using GDHP Technique

Optimal Control of Unknown Discrete-Time Nonlinear Systems w...

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31st Chinese Control Conference

作者： Liu Derong Wang Ding Li Hongliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563811

The adaptive dynamic programming (ADP) approach is employed to design an optimal controller for unknown discrete-time nonlinear systems with control constraints. First, a neural network is constructed to identify the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Moreover, two other neural networks are introduced to approximate the cost function and its derivative and the control law, under the framework of globalized dual heuristic programming technique. Finally, two simulation examples are included to verify the theoretical results.

关键词： adaptive dynamic programming Approximate dynamic programming Control constraints Neural networks Optimal control System identification

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Approximate Optimal Tracking Control for a Class of Discrete-time Nonlinear Systems based on GDHP Iterative Algorithm 34

Approximate Optimal Tracking Control for a Class of Discrete...

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34th Chinese Control and Decision Conference (CCDC)

作者： Tan, Xufeng Li, Yuan Liu, Yang Shenyang Univ Technol Sch Sci Shenyang 110870 Peoples R China Shenyang Univ Technol Sch Elect & Elect Engn Shenyang 110870 Peoples R China

ISBN: (纸本)9781665478960

In this paper, an iterative globalized dual heuristic programming (GDHP) method is applied to optimal tracking control of a class of discrete affine nonlinear systems. In order to further consider the control signal in the varying process, this paper adds a control error difference term to the quadratic cost function. Through system transformation, the optimal tracking problem is transformed into designing the optimal regulator problem for tracking error dynamics. Then, the GDHP algorithm is introduced to deal with the optimal regulation problem. Three neural networks are used as parameter structures to realize the algorithm, which aims at approximating cost function, control law and error dynamics respectively. A simulation example is given to verify the effectiveness of the method.

关键词： adaptive dynamic programming nonlinear system tracking problem global quadratic heuristic programming (GDHP) neural network

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Data-Driven Robust Regulation of Nonlinear Systems With Mismatched Disturbances

Data-Driven Robust Regulation of Nonlinear Systems With Mism...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Yang, Xiong He, Haibo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

ISBN: (纸本)9781538627266

This paper proposes a data-driven robust regulation method of input-affine nonlinear systems with mismatched disturbances. First, the relationship between the robust control problem and the optimal control problem is built, which indicates that the robust control of original systems can be the solution of the optimal control problem of the auxiliary system. Then, within the framework of adaptive dynamic programming, we present a data-driven algorithm to solve the optimal control problem. To implement the data-driven algorithm, we use two kinds of neural networks (NNs): actor NNs are used to approximate the sub-control policies of the augmented control and a critic NN is applied to estimate the value function. To learn the unknown parameters of actor and critic network weight vectors, the Monte Carlo integration method is employed. Finally, we provide a third-order benchmark model of the armature-controlled DC motor to illustrate the applicability of the developed control strategy.

关键词： adaptive dynamic programming DC motor Nonlinear system Optimal control Robust control

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Local Near-Optimal Control for Interconnected Systems with Time-Varying Delays 26th

Local Near-Optimal Control for Interconnected Systems with T...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Wu, Qiuye Lin, Haowei Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9783030367114;9783030367107

In this paper, we develop a novel local near-optimal control (LNOC) for continuous-time (CT) time-varying delayed interconnected systems by observer-critic structure based on adaptive dynamic programming. A boundary function which is approximated by local state observer is presented to substitute the unknown interconnection. Through constructing a novel local value function, the LNOC is obtained by solving the local Hamilton-Jacobi-Bellman equation with the implementation of local critic neural network. By using the Lyapunov's direct method, the proposed LNOC ensures the closed-loop CT time-varying delayed interconnected systems to be asymptotically stable. The effectiveness of the proposed LNOC scheme is illustrated using numerical simulation.

关键词： adaptive dynamic programming Local near-optimal control Unknown time-varying delays Observer-critic architecture Interconnected nonlinear systems

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An Intelligent and Secure Control Approach for Nonlinear Systems under Attacks

An Intelligent and Secure Control Approach for Nonlinear Sys...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Zhong, Xiangnan Ni, Zhen Florida Atlantic Univ Dept Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9781728190488

This paper designs an intelligent and secure control approach based on adaptive dynamic programming for a class of nonlinear systems under the actuator attacks. The designed method can monitor and identify the attacks by an established state estimator based detector. When an attack is triggered, the control process automatically switches to a game-theoretical architecture for attack mitigation. The intelligent learning process is developed for both nominal and attack conditions without the requirement of system dynamics and attack information. Neural network techniques are applied to implement the proposed method with two critic networks and the control signals are calculated accordingly. Therefore, the designed intelligent control method can reduce the computation complexity. Simulation studies and results demonstrate the necessary of attack detection and mitigation during the learning process, and also verify the effectiveness of the developed method.

关键词： adaptive dynamic programming reinforcement learning attack mitigation intelligent control attack detection and identification

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USING ACTOR-CRITIC REINFORCEMENT LEARNING FOR CONTROL AND FLIGHT FORMATION OF QUADROTORS

USING ACTOR-CRITIC REINFORCEMENT LEARNING FOR CONTROL AND FL...

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Torres, Edgar Xu, Lei Sardarmehni, Tohid Univ Texas Rio Grande Valley Brownsville TX USA Kent State Univ Kent OH 44242 USA Calif State Univ Northridge Northridge CA 91330 USA

ISBN: (纸本)9780791886670

This paper introduces a near-optimal controller for the control of quadrotors. A quadrotor is described as a complex, twelve-state system. The paper simplifies the controller by considering it as two levels, the upper-level (kinematics) six-state controller and the lower-level (kinetics) twelve-state controller. An actor-critic optimal controller generates the desired velocities in the upper-level control, and its parameters are tuned by reinforcement learning. The desired velocities are generated using the upper-level controller, which is then used to solve for the lower-level control algebraically. Simulation results are provided to show the effectiveness of the solution.

关键词： Quadrotors reinforcement learning adaptive dynamic programming optimal control

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Barrier Lyapunov Function-Based Safe Reinforcement Learning Algorithm for Autonomous Vehicles with System Uncertainty 21

Barrier Lyapunov Function-Based Safe Reinforcement Learning ...

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21st International Conference on Control, Automation and Systems (ICCAS)

作者： Zhang, Yuxiang Liang, Xiaoling Ge, Shuzhi Sam Gao, Bingzhao Lee, Tong Heng Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China

ISBN: (纸本)9788993215212

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. For such safety-critical systems, it will certainly be a requirement that safe performance should be ensured even during the reinforcement learning period in the presence of system uncertainty. To address this issue, a Barrier Lyapunov Function-based safe reinforcement learning algorithm (BLFSRL) is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges the Barrier Lyapunov Function item into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning when unknown bounded system uncertainty exists. More specifically, the overall system control is optimized with the optimized backstepping technique under the framework of Actor-Critic, which optimizes the virtual control in every backstepping subsystem. Wherein, the optimal virtual control is decomposed into Barrier Lyapunov Function items;and also with an adaptive item to be learned with deep neural networks, which achieves safe exploration during the learning process. Eventually, the principle of Bellman optimality is satisfied through iteratively updating the independently approximated actor and critic to solve the Hamilton-Jacobi-Bellman equation in adaptive dynamic programming. More notably, the variance of control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with motion control problems for autonomous vehicles through appropriate comparison simulations.

关键词： Safe Reinforcement Learning adaptive dynamic programming Barrier Lyapunov Function Autonomous Vehicles

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Neuro-Control for Continuous-Time Stochastic Nonlinear Systems via Online Policy Iteration Algorithm 32

Neuro-Control for Continuous-Time Stochastic Nonlinear Syste...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhou, Tianmin Hou, Jiaxu Li, Handong Di, Zengru Zhao, Bo Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9781728158556

This paper is concerned with the neuro-control for continuous-time nonlinear systems subject to stochastic disturbance. Due to the stochastic disturbance, the traditional value function in existing literature cannot meet the stochastic control problems, since mixed second partial derivatives are employed to construct modified value function of conditional expectation. To solve the Hamilton-Jacobi-Bellman equation, a novel online policy iteration algorithm with an Ito correction term is developed with establishing a critic neural network to approximate the optimal value function. Thus, the online optimal control can be obtained in a closed-loop form. The closed-loop system is guaranteed to be stable in probability via Lyapunov's direct method. Finally, numerical example is provided to illustrate the effectiveness of the developed control method.

关键词： adaptive dynamic programming Reinforcement Learning Policy Iteration Stochastic Nonlinear

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Whole Building Operation Optimal Control System Based on an Occupancy Sensor Network

Whole Building Operation Optimal Control System Based on an ...

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第三十二届中国控制会议

作者： WANG Ming ZHANG Guiqing LI Chengdong Shandong Provincial Key Laboratory of Intelligent Buildings TechnologySchool of Information and Electrical Engineering Shandong Jianzhu University

ISBN: (纸本)9789881563835

Building electromechanical facilities yield more and more significant power *** motivates researchers to work on how to improve building energy ***,it is difficult to improve building energy efficiency at the scale of whole building *** of the difficulty is the lack of building energy management system,but an even greater deficiency arises from absence of optimal algorithms appropriate to all facilities in whole *** address this problem,a integrated optimal control method has been proposed in this *** integrated optimal control system consists of a building facility internet of things,an occupancy sensor network and adaptive dynamic programming *** avoid shortcomings in available sensor technology,cameras,passive infra-red sensors,RFID sensors and CO concentration sensors have been deployed to set up a hybrid occupancy detection sensor ***,a information fusion model based on sensor-mapping spiking neural networks has been put forward as a data analysis tool for determining when and where people occupy a given *** is very critical to control and optimization for complex systems such as the system constituted by all electromechanical systems in a building.A novel adaptive dynamic programming method has been described in this *** learns from environment of a building and generates a series of optimal control strategies to preserve human comfort and improve energy efficiency.

关键词： Building energy conservation adaptive dynamic programming Occupancy detection Spiking neural network Internet of things

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