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检索条件"主题词=Adaptive Dynamic Programming"

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Data-Driven Optimal Control for a Class of Unknown Continuous-Time Nonlinear System Using a Novel ADP Method 7

Data-Driven Optimal Control for a Class of Unknown Continuou...

引用

7th International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Zhang, Kun Zhang, Huaguang Jiang, He Liu, Chong Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781509021550

This paper is concerned with the optimal control problem for a class of unknown continuous-time nonlinear system. A system identification method by date-driven model is established to reconstruct the unknown system dynamic by the input-output data. Then considering the optimal control problem, a novel critic neural networks design is proposed based on the policy iteration (PI), where the updating laws of parameters are designed by the normalized gradient descent algorithm and convex optimization method. And the computational burden of cost error get reduced during the iteration procedure using the new method. Based on this adaptive dynamic programming algorithm, the weight convergence is obtained and stability is guaranteed by Lyapunov theory. Finally, two simulation examples are shown to verify the effectiveness of this novel method.

关键词： optimal control continuous nonlinear system adaptive dynamic programming policy iteration neural network

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H∞ Tracking Control for Unknown Nonlinear System Based on Augmented Matrix Algorithm 9th

<i>H</i>∞ Tracking Control for Unknown Nonlinear System Bas...

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9th International Conference on Modelling, Identification and Control (ICMIC)

作者： Lv, Yongfeng Ren, Xuemei Li, Linwei Na, Jing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789811072123;9789811072116

Based on the augmented matrix and approximate dynamic programming algorithms, the H infinity tracking control problem for unknown nonlinear system is addressed in this paper. An identifier NN is first used to approximate the unknown system. An augmented matrix based on the desired trajectory and system state is then constructed using the identifier, such that the tracking control problem is transformed into the regulation one. We use another NN to approximate the performance index function of the HJI equation, such that H infinity tracking control pairs are calculated without solving the HJI equation. Moreover, we use an estimation algorithm to estimate unknown parameters in neural network. Finally, a simulation is presented to demonstrate the validity of the proposed method.

关键词： H infinity control adaptive dynamic programming Estimation algorithm Augmented matrix

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Value-Iteration-Based Neuro-Optimal Tracking Control for Affine Systems with Completely Unknown dynamics 39

Value-Iteration-Based Neuro-Optimal Tracking Control for Aff...

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39th Chinese Control Conference (CCC)

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China 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 Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (纸本)9789881563903

In this paper, the versatile value-iteration-based control method, aimed at affine systems with unknown dynamics, is proposed to deal with the optimal tracking control problem. Neural networks are adopted to approximate system dynamics and a novel approach is presented to estimate the steady state control input based on the established identifier. Additionally, two other neural networks, called the critic network and the action network, are used to implement the optimal tracking control algorithm. Finally, based on the proposed method, the tracking controller is designed to control a specific simulation example. It is shown that, for any randomly given initial state vector, the controller is able to make the affine system track the reference trajectory without knowing the system dynamics.

关键词： adaptive dynamic programming Neuro-Optimal Tracking Control Value Iteration Affine Systems with Unknown dynamics

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Online Finite-Horizon ADP Algorithm for Solving Non-Cooperative Differential Games 36

Online Finite-Horizon ADP Algorithm for Solving Non-Cooperat...

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36th Chinese Control and Decision Conference (CCDC)

作者： Gao, Yingning Ma, Kemao Harbin Inst Technol Control & Simulat Ctr Harbin Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In this paper, online reinforcement learning solution to finite-horizon non-cooperative differential games is investigated. The main challenges are that, the Hamilton-Jacobi-Isaacs equation is time-varying, and in terms of parameter convergence, conventional learning approaches, with persistence of excitation condition satisfied, may cause undesired oscillations in the system states and deteriorate system performance, especially in finitehorizon situations. Regarding these, we develop a finite-horizon adaptive dynamic programming algorithm for continuous-time systems, where the idea of simulation of experience in concurrent learning technique is adopted to guarantee fast online parameter convergence. Boundedness of the system states and weight estimation errors is proved. Simulation results demonstrate the effectiveness of the presented method.

关键词： finite-horizon differential games reinforcement learning adaptive dynamic programming concurrent learning

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Data-driven Structural Control of Monopile Wind Turbine Towers Based on Machine Learning 21st

Data-driven Structural Control of Monopile Wind Turbine Towe...

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

作者： Zhang, Jincheng Zhao, Xiaowei Wei, Xing Univ Warwick Sch Engn Coventry W Midlands England

This paper studies the data-driven structural control of monopile wind turbine towers based on machine learning approach, by using an active tuned mass damper (TMD) located in the nacelle. The adaptive dynamic programming (ADP) approach is employed to obtain the optimal controller which is derived on the modern large-scale machine learning platform Tensorflow. The proposed network structure includes three simple three-layer neural networks (NNs), i.e. a plant network, a critic network, and an action network. The plant network is used to capture the fully nonlinear dynamics of the structural system while the action network is used to approximate the optimal controller. Their training requires the gradient information flowing through the whole network. The automatic differentiation is used in this paper for all the gradient derivations, which greatly improves the employed ADP algorithm's ability in solving complex practical problems. The simulation results of structural control of monopile turbine towers show that on average the active TMD achieves 15% performance improvement on tower fatigue load reduction over a passive TMD, with small active power consumption (less than 0.24% of the turbine's nominal power production). Besides, the controller design considers the trade-off between control performance and power consumption. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming Reinforcement Learning Neural Networks Floating Wind Turbine Active Structural Control

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State-Dependent adaptive dynamic Programing for a Class of Continuous-Time Nonlinear Systems 3

State-Dependent Adaptive Dynamic Programing for a Class of C...

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3rd International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Batmani, Yazdan Davoodi, Mohammadreza Meskin, Nader Univ Kurdistan Dept Elect Engn Sanandaj Iran Qatar Univ Dept Elect Engn Doha Qatar

ISBN: (纸本)9781509021888

The state-dependent Riccati equation (SDRE) technique can be used to solve optimal control problems for a wide class of nonlinear dynamical systems. In this method, instead of solving a complicated Hamilton-Jacobi-Bellman (HJB) equation, a state-dependent Riccati equation is solved which leads to a suboptimal control law. However, a priori model of the system must be available to apply this technique to the optimal control problem. In this paper, to solve the SDRE without using a priori model of the system, a direct adaptive suboptimal algorithm is proposed. The algorithm, named state-dependent Riccati equation adaptive dynamic programming (SDRE-ADP), is based on a reinforcement learning approach which can be implemented in an online fashion. Like the SDRE technique, the proposed SDRE-ADP can locally asymptotically stabilize the closed-loop system provided that some conditions are satisfied. Application of the proposed algorithm to an autonomous unmanned underwater vehicle (AUV) and a numerical example shows that it can be effectively applied for nonlinear systems.

关键词： adaptive dynamic programming Nonlinear optimal control Reinforcement learning SDRE technique

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UCT-ADP Progressive Bias Algorithm for Solving Gomoku

UCT-ADP Progressive Bias Algorithm for Solving Gomoku

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Cao, Xu Lin, Yanghao Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9781728124858

We combine adaptive dynamic programming (ADP), a reinforcement learning method and UCB applied to trees (UCT) algorithm with a more powerful heuristic function based on Progressive Bias method and two pruning strategies for a traditional board game Gomoku. For the adaptive dynamic programming part, we train a shallow forward neural network to give a quick evaluation of Gomoku board situations. UCT is a general approach in MCTS as a tree policy. Our framework use UCT to balance the exploration and exploitation of Gomoku game trees while we also apply powerful pruning strategies and heuristic function to re-select the available 2-adjacent grids of the state and use ADP instead of simulation to give estimated values of expanded nodes. Experiment result shows that this method can eliminate the search depth defect of the simulation process and converge to the correct value faster than single UCT. This approach can be applied to design new Gomoku AI and solve other Gomoku-like board game.

关键词： adaptive dynamic programming monte carlo tree search gomoku exponential heuristic progressive bias

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Data-Based Approximate Optimal Control for Unknown Nonaffine Systems via dynamic Feedback 12

Data-Based Approximate Optimal Control for Unknown Nonaffine...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Lin, Jinquan 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 Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350321050

In this paper, an integral reinforcement learning (IRL)-based approximate optimal control (AOC) method for unknown nonaffine systems is developed by using dynamic feedback. For optimal control problems of nonaffine systems, optimal control policy cannot be expressed explicitly since the input gain matrix is unknown. Therefore, the nonaffine system is transformed into an augmented affine system by introducing a dynamic feedback signal as the virtual control input. Moreover, by designing an appropriate value function for the augmented affine system, the optimal control of augmented affine system is formulated as the AOC for unknown nonaffine systems. Moreover, the IRL method is adopted to derive the approximate solution of Hamilton-Jacobi-Bellman equation via the critic-only structure. Theoretical analysis concludes that the closed-loop system is uniformly ultimately bounded by using the developed IRL-based AOC scheme. An example is utilized to demonstrate the effectiveness of the present approach.

关键词： adaptive dynamic programming reinforcement learning dynamic feedback data-based control unknown non-affine systems

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Decentralized adaptive Critic Control Design for a Class of Disturbed Nonlinear Interconnected Systems 34

Decentralized Adaptive Critic Control Design for a Class of ...

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34th Chinese Control and Decision Conference (CCDC)

作者： Fan, Wenqian Wang, Ding Li, Menghua Ren, Jin 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 Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

ISBN: (纸本)9781665478960

In this paper, we establish a neural-network-based adaptive dynamic programming (ADP) method to address the decentralized control problem for nonlinear continuous-time (CT) interconnected systems with external disturbances. First, the optimal control schemes of the isolated subsystems are constructed, which are based on the appropriate cost functions. Besides, it is accessible to establish the effective decentralized control strategy of the large-scale systems by adding suitable feedback gains. Then, by taking into account the disturbance rejection issue, the Hamilton-Jacobi-Isaacs (HJI) equation can be derived. To solve the HJI equation, a series of critic neural networks are developed to obtain the cost functions and the optimal control strategies approximately. Additionally, a novel weight updating rule is proposed during the neural critic learning process. Finally, the simulation results are presented to display the decentralized control performance by adopting the neural-network-based ADP method.

关键词： adaptive dynamic programming Decentralized control Disturbance rejection Neural networks Nonlinear interconnected systems Optimal control

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Optimal Control for a Class of Unknown Nonlinear Systems via the Iterative GDHP Algorithm

Optimal Control for a Class of Unknown Nonlinear Systems via...

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8th International Symposium on Neural Networks

作者： Wang, Ding Liu, Derong Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783642210891

Using the neural-network-based iterative adaptive dynamic programming (ADP) algorithm, an optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is proposed in this paper. The optimal controller is designed with convergence analysis in terms of cost function and control law. In order to implement the algorithm via globalized dual heuristic programming (CDHP) technique, a neural network is constructed first to identify the unknown nonlinear system, and then two other neural networks are used to approximate the cost function and the control law, respectively. An example is provided to verify the effectiveness of the present approach.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming intelligent control neural networks optimal control reinforcement learning

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