检索结果-内蒙古大学图书馆

10th World Congress on Intelligent Control and Automation (WCICA)

作者： Zhu, Yuanheng Zhao, Dongbin He, Haibo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

ISBN: (纸本)9781467313988

adaptive dynamic programming (ADP) is an effective method for learning while fuzzy controller has been put into use in many applications because of its simplicity and no need of accurate mathematic modeling. The combination of ADP and fuzzy control has been studied a lot. Before this paper, we have studied using ADP to learn the fuzzy rules of a Monotonic controller, which shows good performance. In this paper, a hyperbolic fuzzy model is adopted to make an improvement. In this way, both membership function and fuzzy rules are learned. With ADP algorithm, fuzzy controller has the capacity of learning and adapting. Simulations on a single cart-pole plant and a rotational inverted pendulum are implemented to observe the performance, even with uncertainties and disturbances.

关键词： adaptive dynamic programming fuzzy control cart-pole plant rotational inverted pendulum

来源：评论

学校读者我要写书评

暂无评论

Observer-critic-based Event-triggered Decentralized Optimal Control of Modular Robot Manipulators

Observer-critic-based Event-triggered Decentralized Optimal ...

引用

第40届中国控制会议

作者： Qiang Pan Bing Ma Bo Dong Xiaolin Ren Yuanchun Li Department of Control Science and Engineering Changchun University of Technology

This paper presents a novel observer-critic-based event-triggered decentralized optimal tracking control of modular robot manipulators(MRMs).Based on the dynamic model of the MRM sub system utilizing joint torque feedback(JTF) technique,the performance index function is constructed by integrating the designed adaptive observer and the tracking error fusion function including position error and velocity *** the basis of adaptive dynamic programming(ADP) algorithm and the eventtriggered mechanism,the decentralized optimal tracking control problem is settled by solving the Hamiltonian-Jacobi-Bellman(HJB) equation via constructing the critic neural network(NN).The closed-loop MRM system is proved to be asymptotically stable by using the Lyapunov stability ***,experimental results illustrate the effectiveness of the developed control method.

关键词： Modular robot manipulators adaptive dynamic programming event-triggered mechanism decentralized optimal control neural network

来源：评论

学校读者我要写书评

暂无评论

Data-Based Iterative DHP Optimal Tracking Control with a Wastewater Treatment Application

Data-Based Iterative DHP Optimal Tracking Control with a Was...

引用

第40届中国控制会议

作者： Huiling Zhao Ding Wang Mingming Zhao Jin Ren Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing University of Technology

In order to implement the tracking control design towards concentrations of dissolved oxygen and nitrate nitrogen in wastewater treatment, a data-based iterative dual heuristic dynamic programming（DHP） method is established in this ***, the optimal tracking control problem is transformed into the optimal regulation problem. Then, the iterative adaptive dynamic programming strategy is introduced to solve the optimal control problem of the new system with the discount factor in the cost function, and the convergence analysis of the iterative algorithm is given. Furthermore, a new training approach is used to adjust the weights of the action and critic networks. Finally, the experimental simulation for a wastewater treatment plant is presented to verify the applicability of the proposed iterative DHP optimal tracking control method.

关键词： adaptive dynamic programming Discount factor Neural networks Optimal tracking control Wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming-based State Quantized Networked Control System without Value and/or Policy Iterations

Adaptive Dynamic Programming-based State Quantized Networked...

引用

International Joint Conference on Neural Networks

作者： Qiming Zhao Hao Xu S. Jagannathan Department of Electrical and Computer Engineering Missouri University of S&T

ISBN: (纸本)9781467314886

In this paper, the Bellman equation is used to solve the stochastic optimal control of unknown linear discrete-time system with communication imperfections including random delays, packet losses and quantization. A dynamic quantizer for the sensor measurements is proposed which essentially provides system states to the controller. To eliminate the effect of the quantization error, the dynamics of the quantization error bound and an update law for tuning its range are derived. Subsequently, by using adaptive dynamic programming technique, the infinite horizon optimal regulation of the uncertain NCS is solved in a forward-in-time manner without using value and/or policy iterations by using Q-function and reinforcement learning. The asymptotic stability of the closed-loop system is verified by standard Lyapunov stability theory. Finally, the effectiveness of the proposed method is verified by simulation results.

关键词： Networked Control System Quantization adaptive dynamic programming Optimal Control Quantization Q FUNCTIONS iterative methods stochastic optimal control Networked control systems infinite horizon asymptotic stability region Closed loop systems tuning Policies Self tuning

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming for Sensor Scheduling in Energy-Constrained Wireless Sensor Networks

Adaptive Dynamic Programming for Sensor Scheduling in Energy...

引用

International Conference on Information Fusion

作者： Wendong Xiao Ruizhuo Song School of Automation and Electrical Engineering University of Science & Technology Beijing 30 XueYuan Road Haidian District Beijing China

ISBN: (纸本)9780982443859

In this paper, we propose an adaptive sensor scheduling scheme to maximize the network lifetime for energyconstrained wireless sensor networks (WSNs) using adaptive dynamic programming (ADP) method. Based on Kalman filter (KF) prediction, the problem is firstly formulated as an infinitestep constrained maximum optimal control problem with the estimation accuracy constraint at each step. Then, a novel adaptive scheduling scheme based on iterative ADP algorithm is proposed as the solution where the predicted performance index is approximated by a neural network. Analysis of the proposed solution is given which shows that the performance index converges to the optimum. A simulation example is employed to illustrate the applicability of the proposed method.

关键词： Wireless sensor networks adaptive dynamic programming sensor scheduling neural networks energyconstrained

来源：评论

学校读者我要写书评

暂无评论

Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games

引用

Science China(Information Sciences) 2019年第12期62卷 164-177页

作者： Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton's method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.

关键词： adaptive dynamic programming reinforcement learning Q-learning fully cooperative linear quadratic dynamic games policy iteration off-policy

来源：评论

学校读者我要写书评

暂无评论

Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture

引用

APPLIED MATHEMATICS AND COMPUTATION 2019年 359卷 344-356页

作者： Zhang, Kun Zhang, Huaguang Mu, Yunfei Sun, Shaoxin Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Fuzzy control Tracking control Integral reinforcement learning T-S fuzzy models adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Self-triggered load frequency control using T-S fuzzy ADP method for unknown power systems

引用

ISA transactions 2025年

作者： Zhongyang Ming Huaguang Zhang Jiayue Sun College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning 110004 China. Electronic address: zhongyangming427@***. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning 110004 China. Electronic address: zhanghuaguang@***. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning 110004 China. Electronic address: jyuesun@***.

Frequency oscillations in interconnected power systems result from the inherent randomness of renewable energy production and fluctuating power load demands. Load frequency control (LFC) has thus emerged as a primary challenge for maintaining power system stability and security due to the synchronization requirements of the entire power grid. This paper proposes a novel approach for LFC in multi-area power systems using a self-triggered control-based adaptive dynamic programming (ADP) framework integrated with fuzzy logic systems (FLSs). First, an H distributed controller is developed based on the multi-agent system (MAS) model to mitigate the effects of parameter uncertainties and load disturbances. Additionally, with the increased deployment of phase measurement units and smart meters, real-time system measurements are expected to rise significantly. This trend underscores the importance of event-triggered control (ETC) in optimizing the use of communication resources. However, general-purpose devices often lack the dedicated hardware necessary to verify triggering rules. To address this limitation, we propose a novel self-triggered control (STC) mechanism. This STC calculates the control law based on the current state to determine the next state measurement, thereby eliminating the need for continuous plant monitoring. Simulation results on a multi-area system demonstrate that the proposed adaptive approach performs effectively in frequency regulation under conditions of load disturbance and parameter uncertainty.

关键词： adaptive dynamic programming Load frequency control Multi-agent system Self-triggered control

来源：评论

学校读者我要写书评

暂无评论

Neural-Network-Based Self-Learning Disturbance Rejection Design for Continuous-Time Nonlinear Constrained Systems

Neural-Network-Based Self-Learning Disturbance Rejection Des...

引用

第40届中国控制会议

作者： Menghua Li Ding Wang Junfei Qiao Xin Xu Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, we adopt a neural-network-based adaptive dynamic programming（ADP） method to solve the disturbance rejection problem for continuous-time nonlinear systems with control constraints. First, we define a suitable function and then derive the optimal control law with control constraints and the worst disturbance law. Besides, the constrained Hamilton-JacobiIsaacs equation for continuous-time nonlinear constrained systems is derived. Then, only one critic neural network is used to approximate the optimal cost function. Consequently, the approximate optimal control law and the approximate worst disturbance law are obtained. Additionally, a new updating rule is developed in the process of neural critic learning. Finally, the simulation results show that the self-learning optimal control is realized and the effectiveness of the disturbance rejection is verified by using the neural-network-based ADP method.

关键词： Optimal control Nonlinear constrained systems adaptive dynamic programming Neural networks Self-learning disturbance rejection H∞ control

来源：评论

学校读者我要写书评

暂无评论

Event-Driven Optimal Regulation with SNAC Framework for Constrained Nonlinear Plants

Event-Driven Optimal Regulation with SNAC Framework for Cons...

引用

第40届中国控制会议

作者： Lingzhi Hu Ding Wang Mingming Zhao Jin Ren Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing University of Technology Beijing Institute of Artificial Intelligence Beijing University of Technology

In order to reduce the computational burden effectively and ensure that the system has good control performance, an event-driven near-optimal control algorithm is developed for discrete-time nonlinear systems with input constraints. A single network adaptive critic algorithm is adopted to address the constrained optimal control problem. First, the optimal cost function and the optimal control strategy are obtained theoretically through the iterative algorithm. Then, an appropriate triggering threshold is established, so that the system satisfies the event-driven mechanism. In addition, the action network of classical adaptive dynamic programming is omitted. The derivative of the approximate cost function is the output of the critic network and then is used to obtain the approximate optimal control law. Furthermore, the convergence analysis of the iterative algorithm is proved. Finally, a simulation example is conducted to verify the effectiveness of the proposed scheme.

关键词： adaptive dynamic programming Control Constraints Discrete-Time Systems Event-Driven Control Neural Networks Single Network adaptive Critic

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：