检索结果-内蒙古大学图书馆

33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zhao, Bo Shi, Guang Li, Chao Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9781538672556

This paper presents an online updated cost function based self-learning optimal control scheme for uncertain nonlinear systems. By establishing an online updated cost function with the help of disturbance observer, the Hamilton-Jacobi-Bellman equation is solved by constructing a critic neural network, whose weight vector is tuned by self-learning algorithm. And then, the optimal control scheme is derived indirectly. Based on Lyapunov stability analysis, the closed-loop system with the proposed scheme is guaranteed to be stable. The simulation results show the effectiveness of the developed self-learning optimal control scheme. The cost function reflects the system uncertainties in real time, which implies that this method relaxes the assumptions on available upper-bounds and matching condition for system dynamics in compared with many existing methods.

关键词： adaptive dynamic programming Uncertain nonlinear systems Optimal control Disturbance observer Neural networks Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

adaptive Learning and Control for MIMO System Based on adaptive dynamic programming

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第7期22卷 1133-1148页

作者： Fu, Jian He, Haibo Zhou, Xinmin Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

关键词： adaptive dynamic programming Levenberg-Marquardt method looper system multiple-input-multiple-output online learning and control

来源：评论

学校读者我要写书评

暂无评论

Optimal Tracking Control of Heterogeneous Multi-agent Systems with Switching Topology via Actor-Critic Neural Networks 37

Optimal Tracking Control of Heterogeneous Multi-agent System...

引用

37th Chinese Control Conference (CCC)

作者： Peng, Zhinan Hu, Jiangping Ghosh, Bijoy K. Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

ISBN: (纸本)9789881563958

In this paper, an optimal tracking control problem is solved for high-order heterogeneous multi-agent systems with time-varying interaction networks within the framework of reinforcement learning. First, the optimal tracking control problem is formulated as a leader-follower multi-agent system. Second, a policy iteration based adaptive dynamic programming (ADP) algorithm is proposed to compute the performance index and the control law. Furthermore, the convergence to the optimal solutions is analyzed for the proposed algorithm. Third, an actor-critic neural network is applied to approximate the iterative performance index function and the control law, which implement the policy iteration algorithm online without using the knowledge of the system dynamics. Finally, some simulation results are presented to demonstrate the proposed optimal tracking control strategy.

关键词： Optimal tracking control multi-agent systems adaptive dynamic programming actor-critic neural network

来源：评论

学校读者我要写书评

暂无评论

adaptive-Learning-Based H_∞ Optimal Tracking Control Design for a Class of Constrained Nonlinear Systems 37

Adaptive-Learning-Based <i>H</i><sub>∞</sub> Optimal Tracki...

引用

37th Chinese Control Conference (CCC)

作者： Hou, Jiaxu Wang, Ding Liu, Derong 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)9789881563958

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H-infinity tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： adaptive dynamic programming H-infinity tracking control constraints convergence proof

来源：评论

学校读者我要写书评

暂无评论

Multi-Sensor Scheduling for Target Tracking based on Constrained ADP in Energy Harvesting WSN 13

Multi-Sensor Scheduling for Target Tracking based on Constra...

引用

13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liu, Fen Jiang, Chengpeng Chen, Shuai Xiao, Wendong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China

ISBN: (纸本)9781538637586

With the development of energy harvesting technologies, the building of wireless sensor networks (WSN) based on energy harvesting has become possible, and helps to weaken the limitation of battery energy in WSN. The main objective of target tracking is to improve the tracking accuracy and to optimize the resource utilization, hence sensor scheduling is essential. Based on an artificial neural network energy acquisition model and extended Kalman filter (EKF) estimation for sensor data fusion, this paper will propose a novel constrained adaptive dynamic programming (ADP) algorithm for multi-sensor scheduling of energy harvesting WSN to optimize the tracking accuracy and resource utilization. Simulation results show that the proposed algorithm is feasible and efficient.

关键词： energy harvesting wireless sensor networks adaptive dynamic programming target tracking extended Kalman filter multi-sensor scheduling

来源：评论

学校读者我要写书评

暂无评论

Critic-Identifier Structure-Based ADP for Decentralized Robust Optimal Control of Modular Robot Manipulators 8

Critic-Identifier Structure-Based ADP for Decentralized Robu...

引用

8th International Conference on Information Science and Technology (ICIST)

作者： Dong, Bo Wang, Shuxiang Zhou, Fan Li, Yan Wang, Shenquan Liu, Keping Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781538637821

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control approach, which combines model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is formulated based on a torque sensing technique that is deployed for each joint module, where the local dynamic information is utilized effectively to design the model compensation controller. A neural network (NN) identifier is established to approximate the dynamics of the interconnected dynamic coupling (IDC). Based on the ADP algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic NN, and the approximate optimal control policy is derived. The closed-loop robotic system is guaranteed to be asymptotic stable by the implementation of a set of decentralized control policies that have been developed. Finally, simulations verify the effectiveness of the proposed method.

关键词： modular robot manipulator decentralized control adaptive dynamic programming optimal control

来源：评论

学校读者我要写书评

暂无评论

ADP-Based Model Reference adaptive Control Design for Unknown Discrete-Time Nonlinear Systems 37

ADP-Based Model Reference Adaptive Control Design for Unknow...

引用

37th Chinese Control Conference (CCC)

作者： Wang, Wei Chen, Xin Wang, Fang Fu, Hao China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)9789881563958

In engineering applications, it is very useful to make a nonlinear system behave like a linear stable system. However, how to design a model reference adaptive control (MRAC) as the nonlinear dynamics is unknown is the key problem. In this study, an MRAC with adaptive dynamic programming (ADP) algorithm is presented for discrete-time nonlinear unknown dynamic systems, in which a multi-layer neural network (NN) model is utilized to describe the nonlinear system's dynamics. Then based on this approximate model, a feedforward neuro-controller is developed as the desired control corresponding to the reference input. Third, the iterative ADP algorithm, in the form of actor-critic framework, employs two NNs to estimate the action value function, and generates the feedback control which, together with the feedforward neuro-controller, makes the state of the system track the reference model. Finally the feasibility of the new approach is verified by two numerical experiments.

关键词： Model reference adaptive control adaptive dynamic programming Nonlinear system Q-function

来源：评论

学校读者我要写书评

暂无评论

Finite horizon optimal control of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming

引用

控制理论与应用（英文版） 2011年第3期9卷 381-390页

作者： Wei, Qinglai Liu, Derong Key Laboratory of Complex Systems and Intelligence Science Institute of Automatio Chinese Academy of Sciences Beijing 100190 China

In this paper,we aim to solve the finite horizon optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming(ADP) approach.A new-optimal control algorithm based on the iterative ADP approach is proposed which makes the performance index function converge iteratively to the greatest lower bound of all performance indices within an error according to within finite *** optimal number of control steps can also be obtained by the proposed-optimal control algorithm for the situation where the initial state of the system is *** networks are used to approximate the performance index function and compute the optimal control policy,respectively,for facilitating the implementation of the-optimal control ***,a simulation example is given to show the results of the proposed method.

关键词： adaptive dynamic programming Unfixed initial state Optimal control Finite time Neural networks

来源：评论

学校读者我要写书评

暂无评论

An Event-Triggered ADP Controller for Single Link Robot Arm System Based on Output Position 30

An Event-Triggered ADP Controller for Single Link Robot Arm ...

引用

30th Chinese Control and Decision Conference (CCDC)

作者： Yang, Yang Xu, Chuang Meng, Qing Tan, Jie Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China

ISBN: (纸本)9781538612446

In this paper, an event -triggered adaptive dynamic programming (ADP) control method is proposed for a single link robot ann system based on the output position. Firstly, we propose a state observer with the neural network (NN) technology to reconstruct the signals of the angular position and angular velocity of the robot arm. Then, in order to solve the optimal control problem of the robot arm system, an ADP controller is introduced in an event-triggered manner based on the inner states from the observer, where the ADP method exploits neural network to approximate the performance index in the system and the event -triggered mechanism is designed to update the control signal aperiodically for the reduction of the computation and transmission load. Finally, as for the continuous and the jump dynamics, the uniform ultimate boundedness (ULM) of the closed-loop system is guaranteed with the help of Lyapunov theory, and simulation results are presented to demonstrate the effectiveness of the proposed controller.

关键词： adaptive dynamic programming Event-Triggered Approach Output Control

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Policy Learning Strategy for Nonlinear Robust Control With Unknown Perturbation 33

Data-Driven Policy Learning Strategy for Nonlinear Robust Co...

引用

33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Fang, Sai Wang, Ding Liu, Derong Ha, Mingming Univ Sci & Technol Beijing Sch Automat Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538672556

In this paper, we propose a robust optimal control policy for nonlinear systems with bounded unknown perturbation by using data-driven policy learning strategy. The robust control problem is transformed into a corresponding optimal control design with specific cost function. Neural-network-based data driven policy learning strategy is presented to solve the problem without system dynamics. The solution of the optimal control problem can asymptotically stabilize the unknown system. An example is given to illustrate the established method.

关键词： adaptive dynamic programming optimal control robust control learning control policy iteration

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：