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Smooth controller design for non-linear systems using multiple fixed models

IET control Theory & Applications 2017年第9期11卷 1467-1473页

作者： Jie Chen Wei Chen Jian Sun The Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing People's Republic of China Science and Technology on Electronic Information Control Laboratory Chengdu People's Republic of China

Multiple model adaptive control (MMAC) with second-level adaptation is a recently proposed methodology for dealing with systems where the parametric uncertainty is large. Compared with the multiple model switching scheme, the new scheme can lead to significant improvements in performance. Some research has been conducted using the new scheme, but all of the results concern linear systems with an adaptive identification model set. In this study, MMAC with second-level adaptation scheme is extended to non-linear systems in strict feedback form, and the fixed identification model set is under consideration. This is motivated by the fact that a smooth controller can lead to smooth performance and the fixed identification model set gains potential advantages over the adaptive identification model set, especially for the case that the parameters of the system change over the time. Design details are presented and the stability of MMAC with second-level adaptation using a fixed identification model set for non-linear systems is given, which has not been discussed before. Finally, two simulations are performed to show that this scheme performs much better than conventional schemes, including adaptive control and multiple-model switching schemes, in terms of convergence speed and transient performance.

关键词： nonlinear control systems control system synthesis adaptive control uncertain systems linear systems identification convergence transient performance convergence speed fixed identification model set second-level adaptation scheme adaptive identification model set linear systems multiple model switching scheme parametric uncertainty MMAC multiple model adaptive control multiple fixed models nonlinear systems smooth controller design

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Performance monitoring and parameter optimization of fuzzy controller in combustion process of coke Oven

Performance monitoring and parameter optimization of fuzzy c...

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Asian control Conference

作者： Qi Lei Di Zhu Xu-Zhi Lai Min Wu School of Information Science and Engineering University of Central South University Changsha China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

The controller parameters can not perform well in the combustion process of coke oven, caused by the fluctuating calories of fuel gas, the changes of coking time and many other exogenous disturbances. In this paper, an optimal method based on performance monitoring by using the trend extraction method, is raised to adjust the parameters of the controllers in the combustion process of coke oven. Firstly, because of the mixture of different kinds of error, the trend extraction method is used to separate trend component from original signal. Secondly, the monitoring indices are designed to examine whether the monitored controller parameters are suitable in the current situation. Then, the performance monitoring result is utilized to choose the decision vector of optimization and determine whether optimization is needed. Finally, simulation results demonstrate that our method performs well in the optimization for combustion process of coke oven in spite of massive changes in the calories of fuel gas.

关键词： Monitoring Combustion Market research Optimization Process control Ovens Heating systems

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Two-layer Fuzzy Kernel Regression for Human Emotional Intention Understanding

Two-layer Fuzzy Kernel Regression for Human Emotional Intent...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Luefeng Chen Mengtian Zhou Min Wu Jinhua She Kaoru Hirota School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China School of Engineering Tokyo University of Technology Tokyo Japan Tokyo Institute of Technology Beijing Institute of Technology Yokohama Japan

A two-layer fuzzy kernel regression (TLFKR) model is proposed for understanding human emotional intention in human-robot interaction, where TLFKR model consists of two layers, including fuzzy c-means (FCM) with kernel ridge regression (Kernel 1) for information analysis layer, fuzzy support vector regressions (FSVR) (Kernel 2) for intention understanding layer. TLFKR model represents the weight impact for each emotional information and aims to improve smooth human-robot interaction by endowing robot with human emotional intention understanding capability. Experimental Results show that the proposal obtains an intention understanding accuracy of 65.67%/68.33%/80.67% with the clusters number c=2/3/6 (according to different genders/ages/nationalities), which are 7.34%/7.18%/8.67% and 18.67%/21.33%/33.67% higher than that of TLFSVR and SVR, respectively. Additionally, preliminary application experiments are performed in the developing emotional social robot system, where two mobile robots and volunteers experience a scenario of "drinking at a bar", and social robots are able to express basic emotions and understand human order intention.

关键词： Kernel Robots Training Support vector machines Proposals Information analysis Bars

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Mean square exponential stabilization of sampled-data Markovian jump systems

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International Journal of Robust and Nonlinear control 2018年第18期28卷

作者： Guoliang Chen Jian Sun Jie Chen 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

In this paper, the problem of mean square exponential stabilization for sampled-data Markovin jump systems is studied. A time-scheduled Lyapunov functional consisting of a exponential-type looped function is constructed using segmentation technology and linear interpolation. Based on this new Lyapunov functional, a less conservative mean square exponential stability criterion is obtained such that a bigger maximum decay rate can be easily calculated. Meanwhile, the quantitative relationship among some system parameters, maximum sampling period, and decay rate is established. Moreover, a time-dependent state feedback sample-data controller is designed. Significant improvements of the proposed exponential-type time-scheduled Lyapunov functional method over some existing ones are verified by numerical examples.

关键词： Markovian jump system mean square exponential stabilization sampled-data control time-scheduled Lyapunov functional

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A weighted-resampling based transfer learning algorithm

A weighted-resampling based transfer learning algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaobo Liu Zhentao Liu Guangjun Wang Zhihua Cai Harry Zhang School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Computer Science China University of Geosciences Wuhan China Faculty of Computer Science University of New Brunswick NB Canada

ISBN: (纸本)9781509061839

Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, and it can be applied to any base learner. In this paper, we propose a weighted-resampling method for transfer learning, called TrResampling. Firstly, resampling is applied to the data with heaven weight in the source domain, and the resampled data is used with the target data as the training data to build a classifier. Then the TrAdaBoost algorithm is used to adjust the weights of source data and target data. We discuss Decision Tree, Naive Bayes, and SVM as the base learner in TrResampling, and choose the suitable for TrResampling. In order to illustrate the performance of the proposed algorithm, we compare TrResampling with the state-of-the-art algorithm TrAdaBoost and the base learner Decision Tree, experimental results on UCI data sets indicate that TrResampling is superior to TrAdaBoost and Decision Tree on many data sets.

关键词： Training data Boosting Machine learning algorithms Support vector machines Decision trees Training Electronic mail

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Downhole-Friction-Estimation-Based Rotary Speed control for Drillstring system with Stick-Slip Vibrations

Downhole-Friction-Estimation-Based Rotary Speed Control for ...

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Asian control Conference

作者： Chengda Lu Min Wu Xin Chen Weihua Cao Chao Gan Jinhua She School of Software and Electrical Engineering Swinburne University of Technology Melbourne Vic Australia School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (纸本)9781509015740;9781509015733

In a drilling process, stick-slip vibrations are one of the main resources resulting in unstable motions or drillstring failures. In this paper, combined with a LuGre bit-rock interaction, the drillstring system is modeled as lumped parameters to capture the main features of stick-slip vibrations. Then, a novel control configuration involving five modules is developed in order to track a desired speed while suppressing the vibration, where the equivalent-input-disturbance method is skilfully employed to suppress such vibrations. In such five modules, the state observer is combined with the friction estimator to obtain the equivalent friction torques, which are corresponding to downhole frictions, on the surface control platform. In addition, the internal model and the state feedback are utilized to get the speed tracking torques so as to guarantee the desired tracking precision. Based on the dual conversion and two quadratic performances, the closed-loop stability is guaranteed by appropriate design of the controller and observer's gain matrices. Simulation is conducted to compare our method with an LQR method, showing our method's superiority and effectiveness.

关键词： Friction Vibrations Observers Torque State feedback

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TIEA: A tradeoff-based interactive multiobjective evolutionary algorithm 7

TIEA: A tradeoff-based interactive multiobjective evolutiona...

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7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Chen, Lu Xin, Bin Chen, Jie School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (纸本)9784990534349

This paper proposes a tradeoff based interactive multiobjective evolutionary algorithm (TIEA). It can support the decision maker (DM) to guide the search towards the DM's most preferred solution by combing evolutionary algorithms with the framework of gradient projection. At each iteration, an evolutionary algorithm is used to generate an approximate Pareto optimal solution. Then, the DM is asked to provide preferences in terms of indifference tradeoffs, the projection of which onto the tangent hyperplane of the Pareto front provides a tradeoff direction. To calculate the projection, an approach to approximate the normal vector of the tangent hyperplane of the Pareto front is developed. Results on nine numerical examples by using TIEA show that TIEA is able to solve multiobjective optimization problems with more than three objectives. Moreover, it can find the Pareto optimal solution that the DM prefers most. © 2016, Fuji Technology Press. All rights reserved.

关键词： Evolutionary algorithms

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External consensus of networked multi-agent systems with nonlinear dynamics and random network delay

External consensus of networked multi-agent systems with non...

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Asian control Conference

作者： Qi Lei Jia-Hui Li Guo-Ping Liu Min Wu School of Information Science and Engineering University of Central South University Changsha China School of Engineering University of South Wales Pontypridd U.K. School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

In this paper, the external consensus problem of networked multi-agent systems with fixed undirected topology, random network delay and nonlinear dynamics is considered. The unknown nonlinear dynamics existing in the systems can be described as RBF-ARX model. Besides, the random network delay can be compensated by introducing the prediction strategy. The RBF-ARX model is used as prediction model to design the output predictor, which is to generate the prediction sequence of control input and agent's output. Then, the designed selector chooses the proper value from the available sequences corresponding to the value of the network delay. Based on the mentioned above, a distributed external consensus control algorithm is proposed to make the considered systems with external reference input achieve consensus. Finally, an example is given to illustrate the validity of the proposed algorithm.

关键词： Delays Nonlinear dynamical systems Algorithm design and analysis Prediction algorithms Predictive models Network topology Radial basis function networks

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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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Sensor fault diagnosis based on on-line random forests 35

Sensor fault diagnosis based on on-line random forests

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35th Chinese control Conference, CCC 2016

作者： Yang, Li Zhang, Jia Deng, Fang Chen, Jie School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control of Complex System Beijing100081 China

ISBN: (纸本)9789881563910

In order to reduce the memory requirement, and obtain real-time status of equipment, the paper proposed to use the the on-line random forests (ORFs) algorithm to identify sensor fault. The sample set is derived from Tennessee Eastman (TE) process. The models are updated by a group of sensor data, which are collected in each interval. As models are real-time and dynamic, the equipment could be tested at any time. Moreover, the samples obtained at previous intervals are not need to store. The results of experiments show that the accuracies of ORFs and Random Forests (RFs) are similar in sensor fault diagnosis process. And in some fast changing process, ORFs distinguishes fault types with higher accuracy, better adaptable and faster than RFs. © 2016 TCCT.

关键词： On-line Random Forests Sensor fault Tennessee Eastman process

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