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第37届中国控制会议

作者： Bin Xin Hepeng Li Ling Wang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Department of Automation Tsinghua University

Interactive multi-objective optimization algorithms have developed rapidly in recent years. In this paper, we propose a new classification-based interactive multi-objective optimization algorithm named ICB-MOEA/D to solve the formulated multiobjective optimization problem. ICB-MOEA/D provides several solutions for the decision maker to choose. The decision maker chooses his/her most preferred solution from these solutions and the historical solutions which have been chosen as the current most preferred solution. ICB-MOEA/D records this solution and classifies the objectives according to the updated preference information into four categories: 1) objectives which are expected to be improved;2) objectives which can be sacrificed;3) objectives which are expected to remain basically unchanged;4) objectives which do not matter currently. Accoding to the number of the objectives in the first category, a new single-objective opitimization model or multi-objective optimization model will be built. The single-objective optimization model will be optimized by a classic variant of differential evolution DE/rand/1/bin, and the multi-objective optimization model will be optimized by a popular docomposition-based multi-objective optimizer MOEA/*** the classifications are done automatically by the algorithm, reducing the burden of the decision maker. ICB-MOEA/D was tested on the two-objective instance ZDT1, and the experiment results show the effectiveness of ICB-MOEA/D.

关键词： interactive multi-objective optimization decomposition classification virtual decision maker

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A Multi-model Estimation of Distribution Algorithm for Agent Routing Problem in Multi-point Dynamic Task

A Multi-model Estimation of Distribution Algorithm for Agent...

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第37届中国控制会议

作者： Sai Lu Bin Xin Lihua Dou Ling Wang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Department of Automation Tsinghua University

The agent routing problem in multi-point dynamic task（ARP-MPDT） is a multi-task routing problem of a mobile agent. In this problem, there are multiple tasks to be carried out in different locations. As time goes on, the state of each task will change nonlinearly. The agent must go to the task points in turn to perform the tasks, and the execution time of each task is related to the state of the task point when the agent arrives at the point. ARP-MPDT is a typical NP-hard optimization problem. In this paper, we establish the nonlinear ARP-MPDT model. A multi-model estimation of distribution algorithm（EDA） employing node histogram models（NHM） and edge histogram models（EHM） in probability modeling is used to solve the ARP-MPDT. The selection ratio of NHM and EHM probability models is adjusted adaptively. Finally, performance of the algorithm for solving the ARP-MPDT problem is verified by the computational experiments.

关键词： Estimation of distribution algorithm Multi-model Routing Multi-point dynamic task

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Stochastic optimal control for sampled-data system under stochastic sampling

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IET control Theory & Applications 2018年第11期12卷 1553-1560页

作者： Haoyuan Sun Jie Chen Jian Sun School of Automation Beijing Institute of Technology Beijing 100081 People's Republic of China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 People's Republic of China

In this study, the problem of designing a stochastic optimal controller for sampled-data systems whose sampling interval is subjected to a certain probability distribution is addressed. To design the controller, the Kronecker product operation and the Vandermonde matrix were introduced. A design method of the stochastic optimal controller is proposed. It is shown that the controller guarantee that the closed-loop system has exponentially mean square stability. Finally, the simulation results illustrate the effectiveness and practicability of the proposed method.

关键词： sampled data systems control system synthesis stochastic systems stochastic processes closed loop systems optimal control asymptotic stability sampling methods statistical distributions matrix algebra stochastic optimal control sampled-data system stochastic sampling sampling interval stochastic optimal controller design probability distribution Kronecker product operation Vandermonde matrix closed-loop system exponentially mean square stability

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A maximum power point tracking controller for thermoelectric generators 36

A maximum power point tracking controller for thermoelectric...

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第36届中国控制会议

作者： Wei Xie Guangyue Huang Xiao Zhang Fang Deng School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems China ZhenHua Oil Co

ISBN: (纸本)9781538629185

Thermoelectric generators（TEGs） directly convert heat energy into electricity for power-supplying sensors and other portable electronic *** this paper,a maximum power point tracking（MPPT） controller in the modified perturb and observe（P＆O） algorithm was designed on the basis of analyzing the output characteristics of the TEG *** MPPT controller consists of a voltage sensor,a current sensor,a microcontroller and a SEPIC converter,which keeps the operating point of the thermoelectric generator tracking the maximum power point（MPP）.The thermoelectric generator was heated to generate electricity,and a supercapacitor accumulated that *** results using the MPPT controller present that the thermoelectric generator’s output voltage achieves around 5V,the output power achieves upward of 2W when the temperature difference △T=75℃,and the operating point of the thermoelectric generator accurately tracks the MPP with a tracking efficiency of 99.8%;the charging power can be improved 89.65%.

关键词： Thermoelectric generators（TEGs） Modified P＆O algorithm Maximum power point tracking（MPPT） SEPIC converter

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Exponential input-to-state stability of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive perturbations

Exponential input-to-state stability of stochastic fuzzy del...

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第37届中国控制会议

作者： Weisong Zhou Hao Zeng Donghai Wu Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and Telecommunications

In this paper,we study a class of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive *** Razumikhin method and iteration technique,some sufficient conditions ensuring the mean-square exponential input-to-state stability of the designed delayed neural networks are *** results can generalize and improve some earlier publications.A numerical example is demonstrated to verify the theoretical results.

关键词： Impulsive Stochastic Cohen-Grossberg neural networks Exponential input-to-state stability

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Distributed Algorithm for Discrete-Time Lyapunov Equations

Distributed Algorithm for Discrete-Time Lyapunov Equations

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International Conference on control, Automation, Robotics and Vision

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Ministry of Education Beijing Institute of Technology Beijing China

This paper investigates the problem of solving a unique solution to discrete-time Lyapunov equations (DTLE) using multi-agent networks. We propose a distributed algorithm where each agent only uses partial information of the matrices. The agents of the algorithm reach a consensus by exchanging information with their neighbors over an undirected connected graph. We provide convergence analysis and the convergence rate estimate for the proposed algorithm. Finally, convergence performance is verified by numerical simulations.

关键词： Distributed algorithms Mathematical model Convergence Optimization Radio frequency Convex functions Symmetric matrices

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Ensemble of Extreme Learning Machines for Regression

Ensemble of Extreme Learning Machines for Regression

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Data Driven control and Learning systems (DDCLS)

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China

ISBN: (纸本)9781538626191

Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett's theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyper parameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.

关键词： Computational modeling Training Mathematical model Predictive models Neural networks Prediction algorithms Approximation algorithms

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Stability of linear systems with sawtooth input delay and predictor-based controller

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Automatica 2020年 117卷

作者： Haoyuan Sun Jian Sun Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 PR China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 PR China School of Electronic and Information Engineering Tongji University Shanghai 200082 PR China

In this paper, the stability of linear systems with sawtooth input delay widely existing in networked systems and predictor-based controller is considered. Under the assumption that there exists an instant where the input delay is zero, a necessary and sufficient condition is obtained to guarantee the exponential stability of the closed-loop system, that is, the closed-loop system is stable if and only if the matrix A + B K is Hurwitz. Two simulation examples are given to confirm the validity of the obtained results.

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Evolutionary Design of controllers with Optimized Structure and Its Application in a Maglev Ball control System 36

Evolutionary Design of Controllers with Optimized Structure ...

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第36届中国控制会议

作者： Zhan Jiaoyang Xin Bin Chen Jie School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

ISBN: (纸本)9781538629185

In order to realize the flexible automatic design of controllers, this paper proposes a library-based controller design method with bi-level optimization of structure and parameters. The controller with optimized structure is composed by components which are selected from a component library. A binary string is used to express a controller structure, where each bit represents the connection relationship between two links. The controller structure and parameters are optimized in a hierarchical manner by the binary Geometric Differential Evolution Algorithm in the outer layer and an advanced Differential Evolution algorithm variant named JADE in the inner layer, respectively. Simulation and experiment of a magnetic levitation ball control system have been done to verify the performance of the presented controller. Results demonstrate the feasibility and superiority of the proposed method.

关键词： Optimal Design of controllers Structural Optimization Library-based controller Design intelligent Optimization Differential Evolution

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Coalition formation based on a task-oriented collaborative ability vector

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 139-148页

作者： Hao FANG Shao-lei LU Jie CHEN Wen-jie CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.

关键词： Collaborative vector Task allocation Multi-agent system Coalition formation Artificial intelligence

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