检索结果-内蒙古大学图书馆

ieee International Conference on Vehicular Electronics and Safety (ICVES)

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781479903801

This paper is concerned with a new iterative adaptive dynamic programming (ADP) algorithm to solve optimal control problems for infinite horizon discrete-time nonlinear systems using a numerical controller. The convergence conditions of the iterative ADP are developed considering the errors by the numerical controller which show that the iterative performance index functions can converge to the greatest lower bound of all performance indices within a finite error bound. Neural networks and digital computer are used to approximate the iterative performance index function and compute the numerically iterative control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the present method.

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

来源：评论

学校读者我要写书评

暂无评论

Data-Driven learning and Control with Multiple Critic Networks

Data-Driven Learning and Control with Multiple Critic Networ...

引用

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

作者： He, Haibo Ni, Zhen Zhao, Dongbin Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467313988

In this paper, we extend our previous work of a three-network adaptive dynamic programming design [1] to be a multiple critic networks design for online learning and control. The key idea of this approach is to develop a hierarchical internal goal representation to facilitate the online learning with detailed and informative internal value signal representations. We present our learning architecture in detail, and also demonstrate its performance on the popular cart-pole balancing benchmark. Simulation results demonstrate the effectiveness of our approach. We also present discussions of further research directions along this topic.

关键词： adaptive dynamic programming (ADP) multiple critic networks external reinforcement signal internal reinforcement signal goal representation hierarchical structure

来源：评论

学校读者我要写书评

暂无评论

An Enhanced reinforcement learning Approach for dynamic Placement of Virtual Network Functions 31

An Enhanced Reinforcement Learning Approach for Dynamic Plac...

引用

31st Annual ieee International symposium on Personal, Indoor and Mobile Radio Communications (ieee PIMRC)

作者： Houidi, Omar Soualah, Oussama Louati, Wajdi Zeghlache, Djamal Inst Polytech Paris Telecom SudParis Samovar UMR 5157 CNRS Paris France Univ Sfax ReDCAD Lab Sfax Tunisia

ISBN: (纸本)9781728144900

This paper addresses Virtualized Network Function Forwarding Graph (VNF-FG) embedding with the objective of realizing long term reward compared to placement algorithms that aim at instantaneous optimal placement. The long term reward is obtained using reinforcement learning (RL), following a Markov Decision Process (MDP) model, enhanced through the injection of expert knowledge in the learning process. A comparison with an Integer Linear programming (ILP) approach, a reduced candidate set (R-ILP), and an algorithm that treats the requests in batch reveals the potential improvements using the RL approach. The instantaneous and short term reward solutions are efficient only in finding instant solutions as they make decisions only on current infrastructure status for a given request at a time or eventually a batch of requests. They are efficient only for present conditions without anticipating future requests. RL possesses instead the learning and anticipation capabilities lacking in instantaneous and snapshot optimizations. A reinforcement learning based approach, called EQL (Enhanced Q-learning), aiming at balancing the load on hosting infrastructures is proposed to achieve the desired longer term reward. EQL employs RL to learn the network and control it based on the usage patterns of the physical resources. Results from extensive simulations, based on realistic and large scale topologies, report the superior performance of EQL in terms of acceptance rate, quality, scalability and achieved gains.

关键词： Network Function Virtualization reinforcement learning dynamic Service Placement Optimization

来源：评论

学校读者我要写书评

暂无评论

Distributed Optimal Consensus Control for Coupled Linear Systems Based on Off-Policy Integral reinforcement learning 39

Distributed Optimal Consensus Control for Coupled Linear Sys...

引用

39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Wenyan Liu, Zhongchang Yang, Xin Dalian Maritime Univ Coll Marine Elect Engn Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This article studies the consensus problem for coupled linear multi-agent systems with completely unknown system dynamics. These coupled individual agent systems aim to achieve consensus for their system states while minimizing a novel type of cost function which can result in distributed control law for each agent system. The optimal distributed controller of each agent is designed using adaptive dynamic programming (ADP). The decoupled value function is derived first, and the model-based ARE is solved iteratively using policy iteration method. Without knowing the system models, the off-policy integral reinforcement learning method is applied, and a linear regression equation is solved to derive the optimal control gains from data collected online. Simulation results verify the effectiveness of the designed algorithm.

关键词： coupled linear systems optimal consensus control reinforcement learning adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

adaptive dynamic programming for terminally constrained finite-horizon optimal control problems 53

Adaptive dynamic programming for terminally constrained fini...

引用

53rd ieee Annual Conference on Decision and Control (CDC)

作者： Andrews, L. Klotz, J. R. Kamalapurkar, R. Dixon, W. E. Univ Florida Dept Mech & Aerosp Engn Gainesville FL USA

ISBN: (纸本)9781467360906

adaptive dynamic programming is applied to control-affine nonlinear systems with uncertain drift dynamics to obtain a near-optimal solution to a finite-horizon optimal control problem with hard terminal constraints. A reinforcement learning-based actor-critic framework is used to approximately solve the Hamilton-Jacobi-Bellman equation, wherein critic and actor neural networks (NN) are used for approximate learning of the optimal value function and control policy, while enforcing the optimality condition resulting from the hard terminal constraint. Concurrent learning-based update laws relax the restrictive persistence of excitation requirement. A Lyapunov-based stability analysis guarantees uniformly ultimately bounded convergence of the enacted control policy to the optimal control policy.

关键词： dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Observer-Based adaptive Synchronization Control of Unknown Discrete-Time Nonlinear Heterogeneous Systems

引用

ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第2期33卷 681-693页

作者： Fu, Hao Chen, Xin Wang, Wei Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China WISDRI Engn & Res Inc Ltd Wuhan 430223 Peoples R China

This article is concerned with the optimal synchronization problem for discrete-time nonlinear heterogeneous multiagent systems (MASs) with an active leader. To overcome the difficulty in the derivation of the optimal control protocols for these systems, we develop an observer-based adaptive synchronization control approach, including the designs of a distributed observer and a distributed model reference adaptive controller with no prior knowledge of all agents' dynamics. To begin with, for the purpose of estimating the state of a nonlinear active leader for each follower, an adaptive neural network distributed observer is designed. Such an observer serves as a reference model in the distributed model reference adaptive control (MRAC). Then, a reinforcement learning-based distributed MRAC algorithm is presented to make every follower track its corresponding reference model on behavior in real time. In this algorithm, a distributed actor-critic network is employed to approximate the optimal distributed control protocols and the cost function. Through convergence analysis, the overall observer estimation error, the model reference tracking error, and the weight estimation errors are proved to be uniformly ultimately bounded. The developed approach further achieves the synchronization by means of synthesizing these results. The effectiveness of the developed approach is verified through a numerical example.

关键词： Synchronization Observers Artificial neural networks Adaptation models Mathematical model Protocols adaptive systems adaptive dynamic programming (ADP) discrete time multiagent systems (MASs) observer optimal synchronization

来源：评论

学校读者我要写书评

暂无评论

Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method

引用

NEUROCOMPUTING 2019年 340卷 158-170页

作者： Yang, Dongsheng Li, Ting Zhang, Huaguang Xie, Xiangpeng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China

In this paper, an online integral reinforcement learning strategy is proposed to deal with robust constrained control problems using event-triggered mechanism for nonlinear Continuous-Time (C-T) systems with external disturbances. The novel design of constrained control law is addressed together with the adaptive event-triggered condition by guaranteeing the optimal performance and system stability. An adaptive online actor-critic Neural Network (NN) reinforcement learning scheme is developed to approximate the optimal solution of the complicated Hamilton-Jacobi-Isaacs equation. Meanwhile, the convergence of NN weight errors and the event-triggered closed-loop system stability are demonstrated to be uniform ultimate bounded by Lyapunov analysis under the proposed triggering condition. Moreover, event-triggered H-infinity tracking control with input constrains and limited network communication is also presented by establishing an augmented system. Finally, simulation results are provided to show the algorithm validity. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Event-triggered control Robust H-infinity control Hamilton-Jacobi-Isaacs (HJI) equation Neural networks Input constrains

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Robust Stabilization of Nonlinear Input-Constrained Systems Using Single Network adaptive Critic Designs

引用

ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第9期50卷 3145-3157页

作者： Yang, Xiong He, Haibo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

In this paper, we study the event-triggered robust stabilization problem of nonlinear systems subject to mismatched perturbations and input constraints. First, with the introduction of an infinite-horizon cost function for the auxiliary system, we transform the robust stabilization problem into a constrained optimal control problem. Then, we prove that the solution of the event-triggered Hamilton-Jacobi-Bellman (ETHJB) equation, which arises in the constrained optimal control problem, guarantees original system states to be uniformly ultimately bounded (UUB). To solve the ETHJB equation, we present a single network adaptive critic design (SN-ACD). The critic network used in the SN-ACD is tuned through the gradient descent method. By using Lyapunov method, we demonstrate that all the signals in the closed-loop auxiliary system are UUB. Finally, we provide two examples, including the pendulum system, to validate the proposed event-triggered control strategy.

关键词： Robustness Optimal control Nonlinear systems Robust control Perturbation methods adaptive systems adaptive critic designs (ACDs) adaptive dynamic programming (ADP) event-triggered control (ETC) input constraints neural network (NN) reinforcement learning (RL)

来源：评论

学校读者我要写书评

暂无评论

Optimal Tracking Control of the Boiler-turbine System Based on adaptive dynamic programming

Optimal Tracking Control of the Boiler-turbine System Based ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Liu, Yujia Gao, Aiguo Wei, Qinglai Chinese Acad Sci Inst Automat Beijing Peoples R China North China Elect Power Res Inst Co Ltd Beijing Peoples R China

ISBN: (纸本)9780738133669

To guarantee the efficient performance of the power plant, an adaptive tacking controller for the nonlinear boiler-turbine system based on offline policy iteration adaptive dynamic prorgamming (ADP) method is proposed in this paper. The optimal tracking controller is obtained through offline learning, which can maintain the characteristics of load changes in drum boiler-turbine type power plants. To implement the proposed method, neural networks (NNs) are used to construct the cost function and approximate optimal solution is achived. Then convergence of the method is analyzed. Simulation studies on the typical boiler-turbine system demonstrate that the proposed control strategy can achieve a satisfactory performance during a short period.

关键词： Integral reinforcement learning adaptive dynamic programming (ADP) boiler-turbine system policy iteration neural network

来源：评论

学校读者我要写书评

暂无评论

Smooth and Stopping Interval Aware Driving Behavior Prediction at Un-signalized Intersection with Inverse reinforcement learning on Sequential MDPs 32

Smooth and Stopping Interval Aware Driving Behavior Predicti...

引用

32nd ieee Intelligent Vehicles symposium (IV)

作者： Yang, Shaoyu Yoshitake, Hiroshi Shino, Motoki Shimosaka, Masamichi Tokyo Inst Technol Dept Comp Sci Tokyo Japan Univ Tokyo Dept Human & Engn Environm Studies Chiba Japan

ISBN: (纸本)9781728153940

Driving behavior modeling (DBM) is widely used in the intelligent vehicle field to prevent accidents, which predicts actions that vehicles should take to optimize safe driving behaviors. According to some statistics, accidents easily happen at un-signalized intersections. Modeling driving behavior at such places is of great importance. However, current inverse reinforcement learning-based DBM methods fail to predict proper behaviors at the un-signalized intersections in the aspects of smoothness and stopping behavior by just using a single Markov decision process (MDP). We propose a novel sequential MDPs approach to model the driving behavior at the un-signalized intersections to solve the problems. Our approach decomposes the target behavior through the un-signalized intersections into three parts and models each decomposition's driving behaviors with appropriate time durations by a stopping-time-interval distribution through dynamic programming. Experiments on real driving data show that the proposed method achieved a better result and successfully improved the smoothness and stopping awareness of the planned driving path compared to the baselines.

关键词： Intelligent vehicles Computational modeling reinforcement learning Predictive models Markov processes dynamic programming Vehicle dynamics

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：