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Asian control Conference

作者： Hao Fu Xin Chen Wei Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

In this paper, a novel online learning controller based on model reference adaptive control (MRAC) is presented to simplify the complexity of controlling a class of discrete nonlinear systems with a little prior knowledge, using approximate dynamic programming (ADP). Without the identifier and the assumption of a command generator model, the proposed approach employs a modified direct heuristic dynamic programming (HDP) to acquire two types of the bounded tracking errors, which are available to transform the system into a linear system with the uncertain bounded persistent disturbance. In addition, toggling between the learning and the sliding mode control (SMC) flexibly, an ADP-to-SMC strategy is developed to guarantee the better performance in MRAC. Finally, Simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： Mathematical model Nonlinear systems Adaptation models Linear systems Computational modeling Linear programming Generators

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Switching control System Based on Robust Model Reference Adaptive control

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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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Maximum power point tracking control of direct-drive permanent magnet wind turbine based on adaptive neuro-fuzzy inference system 5

Maximum power point tracking control of direct-drive permane...

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5th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Ding, Min Ou, Yingxiong Hu, Bo She, Jinhua Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China State Grid Energy Research Institute Beijing102200 China School of Engineering Tokyo University of Technology Tokyo192-0982 Japan

The power from wind varies depending on the environmental factors. Because of the randomness and intermittent of wind, it is difficult for the wind energy conversion system to locate and track the maximum power point (MPP) with fast speed and high accuracy under the condition of variable wind speed. In this paper, a variable-speed wind-generator maximum power point tracking (MPPT) control method based on adaptative neuro-fuzzy inference system (ANFIS) is presented. It is designed as a combination of the Sugeno fuzzy model and neural network. The ANFIS based controller predicts the optimum speed with the inputs as the speed change and power change, and controls the speed with PI controllers. The performance of the proposed controller is tested for direct-drive permanent magnet wind turbine based wind energy conversion system under fast wind speed variation in MATLAB/Simulink. The simulation results show that the designed control method has good dynamic characteristics and steady-state accuracy in wind power MPPT and improves the working efficiency.

关键词： Wind turbines

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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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H∞ state estimation for discrete time-delayed stochastic genetic regulatory networks with known sojourn probabilities 23

H∞ state estimation for discrete time-delayed stochastic ge...

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23rd IEEE International Conference on Automation and Computing, ICAC 2017

作者： Wan, Xiongbo Wang, Zidong Ren, Chuanyu School of Automation China University of Geosciences Wuhan Hubei430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei430074 China Department of Computer Science Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom

ISBN: (纸本)9780701702618

This paper investigates the problem of H∞ state estimation for discrete-time stochastic genetic regulatory networks (GRNs) with switching parameters, time-varying delays, and exogenous disturbances. A new discrete-time stochastic GRN model with switching parameters is proposed by assuming that the sojourn probability of each subsystem of the GRN under consideration is known. By using the stochastic analysis approach, sufficient conditions are derived under which the augmented estimation error system is stochastically stable and satisfies a prescribed H∞ disturbance attenuation level γ. The gain matrices of the desired state estimator can be obtained by solving a matrix inequality. A numerical example is given to illustrate the effectiveness of the designed state estimator. © 2017 Chinese Automation and Computing Society in the UK - CACSUK.

关键词： State estimation

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Optimization Oriented Performance Assessment for Combustion Process of Coke Oven

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IFAC-PapersOnLine 2017年第1期50卷 13778-13783页

作者： Lei, Qi Wu, Min Liu, Guoping School of Information Science and Engineering Central South University ChangshaHunan410083 China School of Automation China University of Geosciences WuhanHubei430074 China School of Engineering University of Southwales PontypriddCF37 1DL United Kingdom Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuhanHubei430074 China CTGT Center Harbin Institute of Technology HaerbinHeilongjiang150001 China

This paper presents an optimization oriented performance assessment method for combustion process of coke oven by integrating analytic hierarchical process (AHP) and intuitionistic fuzzy(IF) set. An AHP model is built up including safety, stability and economic considerations. To handle the complexity in the combustion process of coke oven, where the decision maker usually has some uncertainty in assigning preference values to the index considered, the IF set is used to obtain the IF preference. Entropy and cross entropy are applied to calculate the weights of the individual indices. The comprehensive evaluation method integreated the AHP and IF can effectively solve the performance assessment for the combustion process of the coke oven. Finally, the data obtained from the process model in different operating conditions are used to demonstrate the effectiveness. © 2017

关键词： Coke ovens Coke Combustion Decision making Fuzzy sets Hierarchical systems Analytic hierarchical process Combustion pro cess Comprehensive evaluation Different operating conditions Economic considerations Entropy methods Intuitionistic fuzzy Performance assessment

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Robust kernelized multi-view self-representations for clustering by tensor multi-rank minimization

arXiv

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arXiv 2017年

作者： Qu, Yanyun Liu, Jinyan Xie, Yuan Zhang, Wensheng Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University China State Key Laboratory of Intelligent Control Management of Complex Systems Chinese Academy of Sciences

Most recently, tensor-SVD is implemented on multi-view self-representation clustering and has achieved the promising results in many real-world applications such as face clustering, scene clustering and generic object clustering. However, tensor-SVD based multi-view self-representation clustering is proposed originally to solve the clustering problem in the multiple linear subspaces, leading to unsatisfactory results when dealing with the case of non-linear subspaces. To handle data clustering from the non-linear subspaces, a kernelization method is designed by mapping the data from the original input space to a new feature space in which the transformed data can be clustered by a multiple linear clustering method. In this paper, we make an optimization model for the kernelized multi-view self-representation clustering problem. We also develop a new efficient algorithm based on the alternation direction method and infer a closed-form solution. Since all the subproblems can be solved exactly, the proposed optimization algorithm is guaranteed to obtain the optimal solution. In particular, the original tensor-based multi-view self-representation clustering problem is a special case of our approach and can be solved by our algorithm. Experimental results on several popular real-world clustering datasets demonstrate that our approach achieves the state-of-The-Art performance. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

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A trajectory tracking control strategy based on finite element method for planar three-link underactuated manipulator

A trajectory tracking control strategy based on finite eleme...

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Annual Conference of Industrial Electronics Society

作者： Yawu Wang Xuzhi Lai Haoqiang Chen Pan Zhang Min Wu School of Automation China University of Geosciences Wuhan Hubei China Intelligent Automation for Complex Systems Hubei Key Laboratory of Advanced Control Wuhan Hubei China

This paper presents a trajectory tracking control strategy based on the finite element method for a planar three-link underactuated manipulator with a passive third joint. From the mechanical structure, the manipulator can be thought of as composed of two parts. The first part is the first link of the manipulator, which is a single link manipulator system. The second part consists of the remaining two links, which can be seen as a plananr Pendubot. First, a finite element model of the planar Pendubot is built by using COMSOL, and the coupling relationship between its active link and its passive link is analyzed. Then, two servo controllers (A and B) with the same structure are designed for two active links of the manipulator, respectively. The servo controller A is directly applied to control the angular velocity of the first link of the manipulator rotating with a constant speed, which makes the end-point of the manipulator move along the trajectory direction. Next, a fuzzy system is designed based on the coupling relationship of the planar Pendubot to calculate the target angular velocity of the second link of the manipulator according to the tracking error and its change rate. The servo controller B is applied to control the angular velocity of the second link of the manipulator, which makes the tracking error converge to zero. Finally, Simulation results demonstrate the effectiveness and the robustness of the proposed control strategy.

关键词： Manipulators Servomotors Zirconium Couplings Angular velocity Trajectory tracking Fuzzy systems

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Multi-objective drilling trajectory optimization based on NSGA-II

Multi-objective drilling trajectory optimization based on NS...

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Asian control Conference

作者： Wendi Huang Min Wu Jun Cheng Xin Chen Weihua Cao Yule Hu Hui Gao School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

ISBN: (纸本)9781509015740;9781509015733

Facing the demand of the efficiency and safety of drilling process, it is necessary to find the optimal drilling trajectory in the complex geological environment. To solve this multi-objective optimization problem, non-dominated sorting genetic algorithm II (NSGA-II) is applied. And Linear sum weighted method is applied at the same time for comparison. Firstly, the numerical model of a sidestracking horizontal well trajectory is established by nature curve method. Constraints subjected to engineering conditions and objective functions are discussed. Then, as decision parameters, the build rate and the turn rate are optimized. Finally, optimal solutions distributed on the Pareto front uniformly are obtained.

关键词： Trajectory Linear programming Optimization methods Sociology Statistics Sorting

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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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