检索结果-内蒙古大学图书馆

Fuzzy-Based Goal Representation adaptive dynamic programming

ieee TRANSACTIONS ON FUZZY SYSTEMS 2016年第5期24卷 1159-1175页

作者： Tang, Yufei He, Haibo Ni, Zhen Zhong, Xiangnan Zhao, Dongbin Xu, Xin Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

In this paper, a novel nonlinear learning controller called fuzzy-based goal representation adaptive dynamic programming (Fuzzy-GrADP) is proposed. In the proposed GrADP method, a goal representation network is introduced to generate an adaptive internal reinforcement signal to the critic network to help the controller provide a general mapping between the input and output actions. Moreover, in the proposed architecture, the action network in the GrADP is improved by using the fuzzy hyperbolic model, which combines the merits of the fuzzy model and the neural network model. Based on the back-propagation technique, the parameters in the membership functions and the fuzzy rules are all undergo training and online adapting. The proposed controller is tested on two numerical benchmarks, and the simulation results show that the proposed controller outperforms the original adaptive dynamic fuzzy controller and the pure neural network-based GrADP controller. In addition, the proposed controller is further applied on a large multimachine power system for static var compensator damping control, where simulation results demonstrate the effectiveness of the proposed approach on real applications. Furthermore, in order to demonstrate the theoretical guarantee of the proposed method, Lyapunov stability analysis to support the proposed Fuzzy-GrADP approach has also been carried out.

关键词： adaptive dynamic programming (ADP) fuzzy hyperbolic model (FHM) goal representation adaptive dynamic programming (GrADP) internal goal representation multimachine power systems stability analysis

来源：评论

学校读者我要写书评

暂无评论

Asymptotically Stable adaptive-Optimal Control Algorithm With Saturating Actuators and Relaxed Persistence of Excitation

引用

ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2016年第11期27卷 2386-2398页

作者： Vamvoudakis, Kyriakos G. Miranda, Marcio Fantini Hespanha, Joao P. Univ Calif Santa Barbara Ctr Control Dynam Syst & Computat Santa Barbara CA 93106 USA Univ Fed Minas Gerais Colegio Tecn BR-31270901 Belo Horizonte MG Brazil

This paper proposes a control algorithm based on adaptive dynamic programming to solve the infinite-horizon optimal control problem for known deterministic nonlinear systems with saturating actuators and nonquadratic cost functionals. The algorithm is based on an actor/critic framework, where a critic neural network (NN) is used to learn the optimal cost, and an actor NN is used to learn the optimal control policy. The adaptive control nature of the algorithm requires a persistence of excitation condition to be a priori validated, but this can be relaxed using previously stored data concurrently with current data in the update of the critic NN. A robustifying control term is added to the controller to eliminate the effect of residual errors, leading to the asymptotically stability of the closed-loop system. Simulation results show the effectiveness of the proposed approach for a controlled Van der Pol oscillator and also for a power system plant.

关键词： Approximate dynamic programming (ADP) optimal control reinforcement learning saturating actuators

来源：评论

学校读者我要写书评

暂无评论

ADP-based adaptive optimal tracking of strict-feedback nonlinear systems

ADP-based adaptive optimal tracking of strict-feedback nonli...

引用

ieee symposium Series on Computational Intelligence (SSCI)

作者： Weinan Gao Zhong-Ping Jiang Department of Electrical Engineering Georgia Southern University Statesboro Georgia Department of Electrical and Computer Engineering New York University New York

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. adaptive dynamic programming (ADP) and nonlinear output regulation theories are employed to compute an adaptive near-optimal tracker without a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP.

关键词： Mathematical model adaptive systems Convergence Adaptation models Nonlinear systems learning systems Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP

引用

ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2016年第2期27卷 444-458页

作者： Wei, Qinglai Song, Ruizhuo Yan, Pengfei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. adaptive dynamic programming ( ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

关键词： adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming neurodynamic programming nonlinear systems optimal control recurrent neural network (RNN) reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Enhancing supervisory training signals with environmental reinforcement learning using adaptive dynamic programming and artificial neural networks 15

Enhancing supervisory training signals with environmental re...

引用

15th ieee International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016

作者： Melton, Niklas Wunsch, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9781509038466

A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This paper describes the method of hybridization and presents a model problem in which a human supervisor teaches a simulated car to drive around a race track. Simulation results show successful learning and improvements in convergence time, error rate, and stability over either component method alone. © 2016 ieee.

关键词： reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Dual MPC with reinforcement learning

Dual MPC with Reinforcement Learning

引用

11th IFAC symposium on dynamics and Control of Process Systems including Biosystems

作者： Morinelly, Juan E. Ydstie, B. Erik Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

An adaptive optimal control algorithm for system with uncertain dynamics is formulated under a reinforcement learning framework. An embedded exploratory component, is included explicitly in the objective function of an output feedback receding horizon Model Predictive Control problem. The optimization is formulated as a Quadratically Constrained Quadratic Program and it is solved to epsilon-global optimality. The iterative interaction between the action specified by the optimal solution and the approximation of cost functions balances the exploitation of current knowledge and the need for exploration. The proposed method is shown to converge to the optimal policy for a controllable discrete time linear plant with unknown output parameters. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： adaptive control dual control optimal control model predictive control reinforcement learning approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Discrete-Time Optimal Control Scheme Based on Q-learning Algorithm 7

Discrete-Time Optimal Control Scheme Based on <i>Q</i>-Learn...

引用

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

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

ISBN: (纸本)9781509021550

This paper is concerned with optimal control problems of discrete-time nonlinear systems via a novel Q-learning algorithm. In the newly developed Q-learning algorithm, the iterative Q function in each iteration is required to update on the whole state and control spaces, instead of being updated by a single state and control pair. A new convergence criterion of the corresponding Q-learning algorithm is presented, where the traditional constraints for the learning rates of Q-learning algorithms is relaxed. Finally, simulation results are provided to exemplify the good performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming neuro-dynamic programming Q-learning optimal control neural networks reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：