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第28届中国控制与决策会议

作者： Qinglai Wei Derong Liu Ruizhuo Song Pengfei Yan The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781467397155

In this paper,a new data-based self-learning control scheme is developed to solve infinite horizon optimal control problems for continuous-time nonlinear *** developed optimal control scheme can be implement without knowing the mathematical model of the *** to the input-output data of the nonlinear systems,a recurrent neural network(RNN) is employed to reconstruct the dynamics of the nonlinear *** to the RNN model of the system,a new two-person zero-sum adaptive dynamic programming(ADP) algorithm is developed to obtain the optimal control,where the reconstruction error and the system disturbance are considered the control input of the ***-layer neural networks are used to construct the critic and action networks,which are presented to approximate the performance index function and the control law,***,simulation results will show the effectiveness of the developed data-based ADP methods.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neurodynamic programming Recurrent neural network Data-based control Optimal control

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Optimal Self-Learning control Scheme for Discrete-Time Nonlinear systems Using Local Value Iteration

Optimal Self-Learning Control Scheme for Discrete-Time Nonli...

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International Joint Conference on Neural Networks

作者： Qinglai Wei Derong Liu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509006212

In this paper, an optimal self-learning control scheme for discrete-time nonlinear systems is developed using a new local value iteration based adaptive dynamic programming (ADP) algorithm. The developed local value iteration algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. In the developed local value iteration algorithm, the iterative value function and iterative control law are updated by a subset of the state space. A new analysis method of the convergence property is presented to show that the iterative value functions will converge to the optimum. The convergence criterion for the local value iteration algorithm is presented. A simulation example is given to demonstrate the validity of the present optimal control scheme.

关键词： state-space methods convergence discrete time systems dynamic programming iterative methods learning systems nonlinear systems optimal control

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Adaptive Dynamic Programming Based Fault Compensation control for Nonlinear systems with Actuator Failures

Adaptive Dynamic Programming Based Fault Compensation Contro...

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International Joint Conference on Neural Networks

作者： Bo Zhao Derong Liu Yuanchun Li Guang Shi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing Department of Control Science and Engineering Changchun University of Technology

ISBN: (纸本)9781509006212

This paper develops a novel fault compensation control scheme based on adaptive dynamic programming for nonlinear systems with actuator failures. The control scheme consists of a policy iteration algorithm and a fault compensation. For fault-free dynamic models, the Hamilton-Jacobi-Bellman equation is solved by policy iteration algorithm via constructing a critic neural network, and then the approximate optimal control policy can be derived directly. On the other hand, the online fault compensation is achieved without the fault detection and isolation mechanism by reconstructing the actuator failure. The closed-loop system is guaranteed to be asymptotically stable based on Lyapunov stability theorem. Two simulation examples are given to demonstrate the effectiveness of the present fault compensation control scheme.

关键词： Iterative algorithms Nonlinear systems compensator control dynamic programming Defect detection faults actuators system failures

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A General Adaptive Dynamic Programming Approach with Experience Replay

A General Adaptive Dynamic Programming Approach with Experie...

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International Joint Conference on Neural Networks

作者： Bin Wang Dongbin Zhao Jin Cheng Yuan Xu Yueyang Li School of Electrical Engineering University of Jinan The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Experience replay is a promising approach to improve the learning efficiency of adaptive dynamic programming. A general model-free adaptive dynamic programming (ADP) approach with the experience replay technology is investigated in this paper to solve the optimal control problems in continuous state and action spaces. Both the critic network and action network are modeled with a feedforward neural network with one hidden layer. During the learning process, a number of recently observed data samples are recorded in a database. When updating the parameters of the neural networks, the data in the sample database are repeatedly used to update the weights of the action network and the critic network. Implementation details of the algorithm are given, and simulation experiments are utilized to verify the learning efficiency of the proposed approach.

关键词： Erbium Dynamic programming Neural networks Databases Optimal control Heuristic algorithms Approximation algorithms

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Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design

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Science China(Technological Sciences) 2015年第11期58卷 1915-1923页

作者： QIU HuaXin DUAN HaiBin Bio-inspired Autonomous Flight Systems(BAFS) Research Group Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University

Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization(MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm.

关键词： brushless direct current （BLDC） motor multi-objective pigeon-inspired optimization （MPIO） electromagnetics mul-ti-objective optimization

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Colored petri net based diagnosis of process systems 30

Colored petri net based diagnosis of process systems

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30th European Simulation and Modelling Conference, ESM 2016

作者： Pózna, Anna I. Gerzson, Miklós Leitold, Adrien Hangos, Katalin M. Department of Electrical Engineering and Information Systems Hungary Deparment of Mathematics University of Pannonia P.O.B. 158 VeszprémH-8201 Hungary Process Control Research Group Computer and Automation Research Institute P.O.B. 63 BudapestH-1518 Hungary

ISBN: (纸本)9789077381953

An improved diagnosis method of technological systems is proposed in this paper that is based on their qualitative colored Petri-net model (Leitold et al. 2014). The proposed diagnosis method can be used when more than one fault occur in the system and if the fault evolves during the operation of the system. In order to make the diagnosis of complex process systems computationally feasible, a structural decomposition method is introduced to reduce the size of their occurrence graphs and to make the diagnosis easier. The proposed methods are illustrated on examples.

关键词： Fault detection

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controllability of Heterogeneous Multi-Agent systems 34

Controllability of Heterogeneous Multi-Agent Systems

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第三十四届中国控制会议

作者： GUAN Yongqiang JI Zhijian ZHANG Lin WANG Long School of Automation Science and Electrical Engineering Beihang University College of Automation Engineering Qingdao University Center for Systems and Control Peking University

ISBN: (纸本)9781467374439

This paper addresses the controllability problem for a group of autonomous agents modeled by heterogeneous dynamics, where the communication network is directed and weighted. Two kinds of models for controllability of heterogeneous multi-agent systems are proposed. We show that the heterogeneous multi-agent system is controllable if the underlying communication topology is controllable. Furthermore, some necessary and/or sufficient conditions are derived for controllability from the algebraic and graphical perspectives. Finally, simulation example is presented to demonstrate the effectiveness of the theoretical results.

关键词： Heterogeneous multi-agent system,controllability,Local interaction,Leader-follower framework

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Event-triggered consensus and passification co-design for multi-agent systems over unreliable communication networks

Event-triggered consensus and passification co-design for mu...

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American control Conference

作者： Chao Ma Hong Qiao School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467386838

In this paper, the event-triggered communication strategy with passification consensus co-design is investigated for a class of general multi-agent systems. In particular, the proposed event-triggered mechanism is asynchronous for each agent individually such that the information exchange cost can be reduced effectively. Moreover, with the consideration of unreliable communication networks, randomly occurring transmission failure is modeled by utilizing the stochastic variable. Based on passivity theory, sufficient conditions are established to guarantee that the consensus can be achieved with the desired passification performance. Furthermore, the consensus protocol gain is designed. Finally, a numerical example is provided to demonstrate the effectiveness of our theoretical results.

关键词： Multi-agent systems Symmetric matrices Communication networks Stochastic processes Protocols Eigenvalues and eigenfunctions Information exchange networks(communications) Multi-agent systems information exchange Symmetric matrix Eigenfunctions Markov chain Telecommunications protocols Event trigger Co-design passivity theory Stochastic Processes

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A Binocular Vision-based Measuring System for UAVs Autonomous Aerial Refueling

A Binocular Vision-based Measuring System for UAVs Autonomou...

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IEEE International Conference on control and automation

作者： Yimin Deng Ning Xian Haibin Duan Bio-inspired Autonomous Flight Systems (BAFS) Research Group Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University

ISBN: (纸本)9781509017393

In this paper, we present the systematic design and implementation of a binocular vision-based measuring system for autonomous aerial refueling. The hardware configuration of the verification platform is presented, and vision algorithms including feature extraction and pose estimation are employed for estimating the relation between two rotorcrafts. To verify the autonomous aerial refueling of unmanned aerial vehicles, a binocular vision system and an on-board data processing computer are utilized to provide the real-time pose information. Series of experiments are conducted to demonstrate the feasibility and effectiveness of the overall platform.

关键词： Cameras Receivers Feature extraction Yarn Pose estimation Machine vision Visualization

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Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy iteration algorithm

Neural-network-based robust optimal control of uncertain non...

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International Joint Conference on Neural Networks

作者： Chao Li Ding Wang Derong Liu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 China

ISBN: (纸本)9781509006212

In this paper, we establish a robust optimal control law for a class of continuous-time uncertain nonlinear systems by using a neural-network-based model-free policy iteration approach. The robust control law of the original uncertain nonlinear system is derived by adding a feedback gain to the optimal control law of the nominal system. It is proven that this robust control law can achieve optimality under a specified cost function. Then, the neural-network-based model-free policy iteration algorithm is developed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system without system dynamics. The actor-critic technique and the least squares implementation method are used to obtain the optimal control policy of the nominal system. A numerical simulation is given to verify the applicability of the present robust optimal control scheme.

关键词： Optimal control Cost function Mathematical model Heuristic algorithms Robustness Robust control Nonlinear systems

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