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Switching control system Based on Robust Model Reference Adaptive control

Journal of systems Science & complexity 2016年第4期29卷 897-932页

作者： HU Qiong FEI Qing MA Hongbin WU Qinghe GENG Qingbo School of Automation Beijing Institute of Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology

For conventional adaptive control, time-varying parametric uncertainty and unmodeled dynamics are ticklish problems, which will lead to undesirable performance or even instability and nonrobust behavior, respectively. In this study, a class of discrete-time switched systems with unmodeled dynamics is taken into consideration. Moreover, nonlinear systems are here supposed to be approximated with the class of switched systems considered in this paper, and thereby switching control design is investigated for both switched systems and nonlinear systems to assure stability and performance. For robustness against unmodeled dynamics and uncertainty, robust model reference adaptive control(RMRAC) law is developed as the basis of controller design for each individual subsystem in the switched systems or nonlinear systems. Meanwhile, two different switching laws are presented for switched systems and nonlinear systems, respectively. Thereby, the authors incorporate the corresponding switching law into the RMRAC law to construct two schemes of switching control respectively for the two kinds of controlled systems. Both closed-loop analyses and simulation examples are provided to illustrate the validity of the two proposed switching control schemes. Furthermore, as to the proposed scheme for nonlinear systems, its potential for practical application is demonstrated through simulations of longitudinal control for F-16 aircraft.

关键词： Longitudinal control of F-16 aircraft switching control switching law. nonlinear systems RMRAC switched systems

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A Camera Self-Calibration Method Based on Parallel QPSO

A Camera Self-Calibration Method Based on Parallel QPSO

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第36届中国控制会议

作者： QIU Huang-bin ZHANG Xin-yu DENG Fang GAO Xin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Radio Film & TV Design and Research InstituteBeijing

In the field of machine vision,camera calibration is a key *** self-calibration,one of camera calibration methods,is only based on the images to calculate the camera’s intrinsic *** has simple calibration process and strong *** self-calibration algorithm needs to calculate the epipole and fundamental matrix by solving the Kruppa equation,but the uncertainty of the epipole always leads to large error and long operation *** improve the precision of camera calibration and reduce the time consumption,the parallel quantum particle swarm algorithm（QPSO） is introduced to solve the improved Kruppa *** can figure out the camera intrinsic parameters and transform the calculation of epipole into the adaptive value of the cost *** with the ordinary particle swarm optimization algorithm（PSO）,QPSO has less parameters,better robustness and faster convergence *** using a multi-core computer platform,its parallel processing has also combined with the characteristics of parallel computing which improves the calculation *** results show that the proposed method is more accurate than ordinary PSO,and the program time consuming is significantly reduced.

关键词： parallel computing quantum particle swarm algorithm（QPSO） camera self-calibration Kruppa equation

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Comparative study of VSP binning methods for estimating fuel consumptions of LDVs on urban roads

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AIP Conference Proceedings 2019年第1期2066卷

作者： Qi Zhao Qi Chen Guohua Song Lei Yu 1Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing 100144 China 2MOE Key Laboratory for Urban Transportation Complex System Theory and Technology Beijing Jiaotong University Beijing 100044 China 3College of Science and Technology Texas Southern University Houston Texas 77004 United States

The micro-scale fuel consumption model is a tool commonly used to evaluate the effect of traffic flow conditions on vehicle fuel consumptions, which is an essential step in developing traffic management strategies for saving fuels. In developing any micro-scale fuel consumption model, the Vehicle Specific Power (VSP) binning method has been widely adopted in recent years as a fundamental approach. Existing studies have shown that the high distribution frequency when VSP=0 contributes to high fuel consumptions, so a question quickly emerged about whether VSP=0 should be designed as an independent VSP bin in the VSP binning method. This paper strives to compare different VSP binning methods for Light-Duty Vehicles (LDVs) on urban roads in Beijing in terms of their effects on the estimation of fuel consumptions. After collecting and processing field vehicle activity data and fuel consumption data, three VSP binning methods are proposed for the study. Then, total fuel consumptions and fuel consumption factors are calculated by using the proposed VSP binning methods as well as the second-by-second and average travel speed data. Finally, the estimation accuracy of total fuel consumptions and fuel consumption factors based on the three different VSP binning methods are compared for LDVs on urban roads. The study proves that an independent VSP bin at VSP=0 is indeed necessary, which can improve the estimation accuracy on both total fuel consumptions and fuel consumption factors.

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Dependency-Enhanced Reordering Model for Chinese-English SMT 35

Dependency-Enhanced Reordering Model for Chinese-English SMT

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第35届中国控制会议

作者： WANG Miaomiao XIE Guo DU Jinhua Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

This paper proposes a dependency-enhanced pre-reordering method for Chinese-English statistical machine translation(SMT).Firstly,two kinds of dependency structure-based rules are extracted based on the source-side dependency tree and corresponding word alignments between the source-side and the target-side *** a maximum entropy classifier is used to calculate the orientation probability in terms of swap or monotone between two *** a result,a reordering rule set is *** different ways are proposed to filter out the rule ***,the dependency parsing trees of the training data,development set and the test set are traversed,and if the syntactic sub-tree structure matches the rules in the rule set,the word orders will be ***,a reordered source-side sentence is generated and then fed into an SMT system for *** conducted on NIST Chinese-English MT data sets show that the proposed method significantly improves translation performance by 0.46 BLEU compared to the baseline system.

关键词： Statistical Machine Translation Word Alignment Dependency Syntactic Parsing

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The Identification of Stochastic Time-varying Parameters for Nonlinear system 35

The Identification of Stochastic Time-varying Parameters for...

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第35届中国控制会议

作者： XIE Guo ZHANG Dan QIAN Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

In this paper, a method for a sort of nonlinear system identification with stochastic time-varying parameter is investigated. This kind of nonlinear systems is referring to the system where probability density functions(PDFs) of the parameters are known. This parameter identification and states estimation method is realized based on expectation maximization(EM) algorithm and particle filter. Firstly, parameter particles are generated randomly according to the PDF of parameter. Secondly, the particle filter is employed to estimate system states corresponding to each group of the parameters, and the weight of each group parameters is calculated according to the Bayesian theory. Then the new iteration of parameter is obtained by adopting the expectation maximization algorithm. Lastly, the real parameters are obtained along with system operation. Numerical illustrations are presented to exhibit the effectiveness of the method proposed herein, and the performance of the method is examined.

关键词： Stochastic time-varying parameter Parameter identification Nonlinear system Expectation Maximization algorithm(EM)

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Robust sampled-data control for Itô stochastic Markovian jump systems with state delay

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International Journal of Robust and Nonlinear control 2018年第15期28卷

作者： Guoliang Chen Jian Sun Jianwei Xia School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China School of Mathematics Science Liaocheng University Liaocheng China

In this paper, the problem of robust sampled-data control for Itô stochastic Markovian jump systems (Itô SMJSs) with state delay is investigated. Using parameters-dependent Lyapunov functionals and some stochastic equations, we give stochastic sufficient stability criteria for polytopic uncertain Itô SMJSs. As a corollary, stochastic sufficient stability criteria are given for nominal Itô SMJSs. For this two cases of Itô SMJSs, based on the obtained stochastic stability criteria, their time-independent sampled-data controllers are designed, respectively. Then, for designing a time-dependent sampled-data controller for Itô SMJSs, a parameters-dependent time-scheduled Lyapunov functional is developed. New stochastic sufficient stability criteria are obtained for polytopic uncertain Itô SMJSs and nominal Itô SMJSs. Furthermore, their time-dependent sampled-data controllers are designed, respectively. Lastly, a numerical example is provided to illustrate the effectiveness of the proposed method.

关键词： Itô stochastic Markovian jump systems parameters-dependent time-scheduled Lyapunov functional robust sampled-data control stability

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Improved Nonsingular Terminal Sliding Mode Attitude Tracking control for Reentry Vehicle 35

Improved Nonsingular Terminal Sliding Mode Attitude Tracking...

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第35届中国控制会议

作者： CHENG Yifeng SHENG Yongzhi LIU Xiangdong School of Automation Beijing Institute of Technology Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781509009107

This paper investigates the attitude tracking control problem of reentry vehicle with modeling inaccuracies and external disturbances. An improved nonsingular terminal sliding mode control method is presented, and a faster convergence rate is obtained in comparison with the conventional nonsingular terminal sliding mode control(NTSM). By the proposed control strategy, the possible singularity during the control phase is avoided, and robustness is also guaranteed. Simulation is made for a reentry vehicle in the condition aerodynamic parameters and atmospheric density are perturbed. The results show the effectiveness of the proposed strategies.

关键词： reentry vehicle improved nonsingular terminal sliding mode fast convergence rate Lyapunov stability singularity elimination

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New advances in complex motion control for single robot systems and multi-agent systems

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Science China(Technological Sciences) 2016年第12期59卷 1963-1964页

作者： FANG Hao LU Shao Lei CHEN Jie School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China

CHEN Jie,email：chenjie@***

关键词： robot advances email cooperation simplified decomposed scenarios classic climbing obstacles

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Model predictive control for UGV trajectory tracking based on dynamic model

Model predictive control for UGV trajectory tracking based o...

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2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016

作者： Meiling, Wang Zhen, Wang Yi, Yang Mengyin, Fu Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781509041022

In this paper, a simplified dynamic vehicle model is established to accurately describe the dynamics of Unmanned Ground Vehicle (UGV) in trajectory tracking, while meeting the real-Time computing requirement. And a modified model predictive control (MPC) algorithm with soft constraint for UGV trajectory tracking is proposed to improve the tracking stability and rapidity. The optimal control problem at each sampling time is converted into a quadratic program (QP), which has mature solutions. To verify the trajectory tracking capabilities, the proposed MPC controller is compared with a PD controller under different longitudinal velocities. The simulation results demonstrate that the MPC controller can effectively reduce the tracking error and ensure the vehicle's traveling smoothness. © 2016 IEEE.

关键词： Model predictive control

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Spike-burst chimera states in an adaptive exponential integrate-and-fire neuronal network

arXiv

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arXiv 2019年

作者： Santos, Moises S. Protachevicz, Paulo R. Iarosz, Kelly C. Caldas, Iberê L. Viana, Ricardo L. Borges, Fernando S. Ren, Hai-Peng Szezech, José D. Batista, Antonio M. Grebogi, Celso Department of Physics Federal University of Paraná CuritibaPR80060-000 Brazil Graduate in Science Program - Physics State University of Ponta Grossa Ponta Grossa PR84030-900 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xi'an710048 China Xian Technological University Xi'an710021 China Graduate in Science Program - Physics State University of Ponta Grossa Ponta Grossa PR84030-900 Brazil Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen AberdeenAB24 3UE United Kingdom

Chimera states are spatiotemporal patterns in which coherence and incoherence coexist. We observe the coexistence of synchronous (coherent) and desynchronous (incoherent) domains in a neuronal network. The network is composed of coupled adaptive exponential integrate-and-fire neurons that are connected by means of chemical synapses. In our neuronal network, the chimera states exhibit spatial structures both with spikes and bursts activities. Furthermore, those desynchronised domains not only have either spike or burst activity, but we show that the structures switch between spikes and bursts as the time evolves. Moreover, we verify the existence of multicluster chimera states. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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