检索结果-内蒙古大学图书馆

adaptive dynamic programming-based decentralised control for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay

引用

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2020年第15期51卷 2883-2898页

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

This paper addresses decentralised optimal control problems for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay by adaptive dynamic programming. To eliminate the effects of mismatched interconnections with unknown time-delay, a novel local value function is constructed to transform the decentralised control problem into an optimal control problem. A local robust observer is established to identify the bound of the unknown interconnections. Then, based on the observer-critic architecture, the decentralised optimal control policy is achieved by solving local Hamiltonian-Jacobi-Bellman equation via local policy iteration algorithm. The stability of the closed-loop large-scale nonlinear system is guaranteed to be uniformly ultimately bounded by implementing a set of decentralised control policies. Simulation examples demonstrate the effectiveness of the proposed scheme.

关键词： adaptive dynamic programming large-scale nonlinear systems decentralised control unknown mismatched interconnection unknown time-delay reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming and Zero-Sum Game-Based Distributed Control for Energy Management Systems With Internet of Things

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第24期10卷 22371-22385页

作者： Tan, Luy Nguyen Gupta, Nishu Derawi, Mohammad Ho Chi Minh City Univ Technol Fac Elect & Elect Engn Ho Chi Minh City Vietnam Vietnam Natl Univ Dept Automat Control Ho Chi Minh City 700000 Vietnam Norwegian Univ Sci & Technol Fac Informat Technol & Elect Engn Dept Elect Syst N-2815 Gjovik Norway

Energy management systems (EMS) in smart grids provide end users with the optimal operational efficiency of power from nonsmart microgrids, including power grids, energy storage systems (ESS), and residential loads. This article proposes a novel distributed online control policy for Ambient Intelligence (AmI)-based Internet of Things (IoT) environments, optimizing a consensus utility function, including electricity cost and the lifespan of ESS. Different from the existing methods, the distributed EMS via IoT can gain cooperative L(2 )performance by rejecting external disturbances and providing consensus policies for robust optimal charging and discharging. First, consensus dynamics of AmI-agents are constructed, and the Hamilton-Jacobi-Isaacs (HJI) equations are established, where the Nash equilibrium points are approximated by ADP and zero-sum game theory. Second, with the aid of an actor-critic structure, a robust optimal distributed control algorithm in an online manner for EMS is proposed. Therefore, collecting sample sets and training offline are completely avoided. Third, to deal with the unknown internal dynamics of ESS, the Q -learning algorithm is employed instead of system identification techniques that require available sample sets. The algorithm guarantees that the global load is balanced and that the consensus tracking error and the function approximation error are uniformly ultimately bounded. Finally, numerical simulations are provided to verify the effectiveness of the proposed algorithm for a large-scale system of nonsmart microgrids.

关键词： adaptive dynamic programming energy management energy storage Internet of Things (IoT) zero-sum game

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming as a theory of sensorimotor control

引用

BIOLOGICAL CYBERNETICS 2014年第4期108卷 459-473页

作者： Jiang, Yu Jiang, Zhong-Ping NYU Polytech Sch Engn Dept Elect & Comp Engn Control & Networks LabMetrotech Ctr 5 Brooklyn NY 11201 USA

Many characteristics of sensorimotor control can be explained by models based on optimization and optimal control theories. However, most of the previous models assume that the central nervous system has access to the precise knowledge of the sensorimotor system and its interacting environment. This viewpoint is difficult to be justified theoretically and has not been convincingly validated by experiments. To address this problem, this paper presents a new computational mechanism for sensorimotor control from a perspective of adaptive dynamic programming (ADP), which shares some features of reinforcement learning. The ADP-based model for sensorimotor control suggests that a command signal for the human movement is derived directly from the real-time sensory data, without the need to identify the system dynamics. An iterative learning scheme based on the proposed ADP theory is developed, along with rigorous convergence analysis. Interestingly, the computational model as advocated here is able to reproduce the motor learning behavior observed in experiments where a divergent force field or velocity-dependent force field was present. In addition, this modeling strategy provides a clear way to perform stability analysis of the overall system. Hence, we conjecture that human sensorimotor systems use an ADP-type mechanism to control movements and to achieve successful adaptation to uncertainties present in the environment.

关键词： Optimal control Motor learning Endpoint stiffness adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming and optimal control of nonlinear nonaffine systems

引用

AUTOMATICA 2014年第10期50卷 2624-2632页

作者： Bian, Tao Jiang, Yu Jiang, Zhong-Ping NYU Polytech Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA

In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： adaptive optimal control adaptive dynamic programming Nonaffine systems

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming and Cooperative Output Regulation of Discrete-time Multi-agent Systems

引用

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2018年第5期16卷 2273-2281页

作者： Gao, Weinan Liu, Yiyang Odekunle, Adedapo Yu, Yunjun Lu, Pingli Georgia Southern Univ Allen E Paulson Coll Engn & Comp Dept Elect & Comp Engn Statesboro GA 30460 USA Chinese Acad Sci Shenyang Inst Automat Beijing 110016 Peoples R China Nanchang Univ Informat Engn Sch Dept Elect & Automat Engn Nanchang 330029 Jiangxi Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this paper, a novel data-driven control solution to cooperative output regulation problems is proposed for a class of discrete-time multi-agent systems. Different from existing solutions to cooperative output regulation problems, the dynamics of all the followers are presumed unknown. Based on the combination of the internal model principle and the value iteration technique, a distributed suboptimal controller is learned by means of online input-state data collected from system trajectories. Notably, the developed learning algorithm does not rely on a priori knowledge of a stabilizing control policy. Rigorous theoretical analysis guarantees the convergence of the algorithm and the stability of the closed-loop system. Numerical results validate the effectiveness of the proposed control methodology.

关键词： adaptive dynamic programming cooperative control multi-agent systems output regulation value iteration

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming for Multi-Driver Order Dispatching at Large-Scale

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第2期10卷 607-621页

作者： Jiang, Kai Cao, Yue Zhou, Huan Wu, Jie Zhang, Zhao Liu, Zhi Wuhan Univ Sch Cyber Sci & Engn Wuhan 430000 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710129 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Univ Electrocommun Dept Comp & Network Engn Tokyo 1828585 Japan

Order dispatching, which involves assigning orders to demand-matched vehicles, is an underlying issue for ride-sharing services. Previous works on order dispatching are often quasi-static and myopic 1, thus performing unsatisfactorily in the ride-sharing setting. To address these challenges, recent studies attempt to augment large-scale decision optimization from a data-driven perspective. Among them, adaptive dynamic programming (ADP) has exhibited its particular potential for sequential decision-making with a long-term objective under uncertainty. In this paper, we investigate order dispatching with consideration of vehicle repositioning by exploiting ADP. We first formulate the optimization problem as a Markov Decision Process (MDP), where the dispatching decision is determined by a series of agents (the decision-making entity) under the time sequence model. Then, based on the generated available trips by a graph theory-based method, an ADP-based Multi-driver Order Dispatching method (AMOD) is proposed. In particular, AMOD reconstructs the Bellman update process around the post-decision states to avoid approximating the embedded expectations explicitly. As for non-linear function approximation, it converts the value function into a linear combination by a quadratic decomposition, and estimates the decomposed value function with neural network-based parameter approximation. In addition, vehicle repositioning is performed along with each batch dispatching to balance ride supply across geographic dimensions. Extensive simulations are conducted based on real-world data. Especially, AMOD can achieve 34.6% improvement at maximum and 15.9% on average compared with other baselines, when the capacity constraint is 10.

关键词： Dispatching Vehicle dynamics Decision making Optimization Adaptation models Uncertainty Real-time systems Order dispatching adaptive dynamic programming graph theory Markov decision process

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming for containment control with robustness analysis to iterative error: A global Nash equilibrium solution

引用

ISA TRANSACTIONS 2024年 154卷 132-146页

作者： Chen, Zitao Chen, Kairui Wang, Jianhui Southeast Univ Sch Math Nanjing 211189 Jiangsu Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China

Global Nash equilibrium is an optimal solution for each player in a graphical game. This paper proposes an iterative adaptive dynamic programming-based algorithm to solve the global Nash equilibrium solution for optimal containment control problem with robustness analysis to the iterative error. The containment control problem is transferred into the graphical game formulation. Sufficient conditions are given to decouple the Hamilton-Jacobi equations, which guarantee the solvability of the global Nash equilibrium solution. The iterative algorithm is designed to obtain the solution without any knowledge of system dynamics. Conditions of iterative error for global stability are given with rigorous proof. Compared with existing works, the design procedures of control gain and coupling strength are separated, which avoids trivial cases in the design procedure. The robustness analysis exactly quantifies the effect of the iterative error caused by various sources in engineering practice. The theoretical results are validated by two numerical examples with marginally stable and unstable dynamics of the leader.

关键词： adaptive dynamic programming Containment control Global Nash equilibrium Multi-agent system

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming Algorithm for Renewable Energy Scheduling and Battery Management

引用

COGNITIVE COMPUTATION 2013年第2期5卷 264-277页

作者： Boaro, Matteo Fuselli, Danilo De Angelis, Francesco Liu, Derong Wei, Qinglai Piazza, Francesco Univ Politecn Marche Dipartimento Ingn Informaz I-60131 Ancona Italy Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The employment of intelligent energy management systems likely allows reducing consumptions and thus saving money for consumers. The residential load demand must be met, and some advantages can be obtained if specific optimization policies are taken. With an efficient use of renewable sources and power imported from the grid, an intelligent and adaptive system which manages the battery is able to satisfy the load demand and minimize the entire energy cost related to the scenario under study. In this paper, an adaptive dynamic programming-based algorithm is presented to face dynamic situations, in which some conditions of the environment or habits of customer may vary with time, especially using renewable energy. Based on the idea of smart grid, we propose an intelligent management scheme for renewable resources combined with battery implemented with a faster and simpler scheme of dynamic programming, by considering only one critic network and some optimization policies in order to satisfy the load demand. Since this kind of problem is suitable to avoid the training of an action network, the training loop among the two neural networks is deleted and the training process is greatly simplified. Computer simulations confirm the effectiveness of this self-learning design in a typical residential scenario.

关键词： adaptive dynamic programming Approximate dynamic programming Neural networks Energy scheduling Battery management

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming based event-triggered control for unknown continuous-time nonlinear systems with input constraints

引用

NEUROCOMPUTING 2020年 396卷 191-200页

作者： Xue, Shan Luo, Biao Liu, Derong Li, Yueheng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

An adaptive dynamic programming (ADP) based event-triggered control method is established in this paper to solve the optimal control problem of unknown continuous-time nonlinear systems with input constraints. First, the unknown system is identified using two neural networks (NNs). Second, the threshold for event-triggering condition is designed, which guarantees the system stability. Then, a critic NN is employed to approximate the value function and the closed-loop system is proved to be uniformly ultimately bounded. Finally, the simulation results demonstrate the feasibility of the developed ADP method. The main contributions of this paper are that the developed ADP-based event-triggered control method avoids the Zeno phenomenon effectively and updates the weights of three NNs simultaneously in the process of implementation. Meanwhile, an improved critic NN weight updating criterion is adopted, which does not require an initial admissible control. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Event-triggered control adaptive dynamic programming Input constraints Neural network

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming-based stabilization of nonlinear systems with unknown actuator saturation

引用

NONLINEAR dynamicS 2018年第4期93卷 2089-2103页

作者： Zhao, Bo Jia, Lihao Xia, Hongbing Li, Yuanchun Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Beijing 100190 Peoples R China Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China

This paper addresses the stabilizing control problem for nonlinear systems subject to unknown actuator saturation by using adaptive dynamic programming algorithm. The control strategy is composed of an online nominal optimal control and a neural network (NN)-based feed-forward saturation compensator. For nominal systems without actuator saturation, a critic NN is established to deal with the Hamilton-Jacobi-Bellman equation. Thus, the online approximate nominal optimal control policy can be obtained without action NN. Then, the unknown actuator saturation, which is considered as saturation nonlinearity by simple transformation, is compensated by employing a NN-based feed-forward control loop. The stability of the closed-loop nonlinear system is analyzed to be ultimately uniformly bounded via Lyapunov's direct method. Finally, the effectiveness of the presented control method is demonstrated by two simulation examples.

关键词： adaptive dynamic programming Unknown actuator saturation Continuous-time nonlinear systems Stabilizing control Neural networks

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：