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Output Feedback Adaptive Robust Learning control of a Class of Nonlinear Systems with Periodic Disturbances

IFAC Proceedings Volumes 2011年第1期44卷 4874-4879页

作者： Xiangbin Liu Zhongsheng Hou Bin Yao Hongye Su Mingxuan Sun Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China. (Tel: 86-10-5168-4105) School of Mechanical Engineering Purdue University West Lafayette IN 47907 U.S.A. State Key Lab. of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P. R. China College of Information Engineering Zhejiang University of Technology Hangzhou 310032 China

Abstract In this paper, a discontinuous projection-based output feedback adaptive robust learning control (OARLC) scheme is constructed for a class of nonlinear systems in a semi-strict feedback form by incorporating an observer and a dynamic normalization signal. Since only output signal is available for measurement, an observer is firstly designed to provide exponentially convergent estimates of the unmeasurable states. Using certain known basis functions to capture the characteristics of unknown general periodic disturbances, the discontinuous projection type adaptation law can then be used to tune the amplitudes of those basis functions on-line to recover the unknown general periodic disturbances asymptotically. The estimation errors due to the unknown initial states, uncompensated disturbances, and the uncertain nonlinearities are also effectively dealt with via certain robust feedback at each step of the proposed OARLC backstepping design. The resulting controller achieves a guaranteed transient and a prescribed final tracking accuracy for output tracking performance. In addition, when the general periodic disturbances fall within the approximation ranges of the periodic basis functions, asymptotic output tracking performance is achieved as well.

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Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling

Adaptive least contribution elimination kernel learning appr...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010

作者： Gao, Yan-Chen Ji, Jun Wang, Hai-Qing Li, Ping State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou China

ISBN: (纸本)9781424465835

Rubber mixing process is a typical non-linear fed-batch process without well developed mechanism. Soft-sensing modeling of the mixture's Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay. Furthermore, the mixing process is drifting and volatile even noisy;only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAl) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances. ©2010 IEEE.

关键词： Rubber

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Adaptive local weighted kernel-based regression for online modeling of batch processes

Adaptive local weighted kernel-based regression for online m...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010

作者： Chen, Kun Wang, Haiqing Ji, Jun Song, Zhihuan Liu, Yi State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9781424465835

Fed-batch processes are inherently more difficult to characterize than continuous processes due to the variations under different operation stages, drifting and small-sample condition. The classical kernel-based regression (KR) methods, e.g., least squares support vector regression (LSSVR), aim to achieve a universal generalization performance, which may fail in some local regions when applied to batch process modeling. Local LSSVR model which only uses the neighbors of the query instance helps improve the accuracy, but it generally leads to a heavy computation load. Inspired by the idea of universal and local learning simultaneously, an adaptive local weighted kernel-based regression (ALW-KR) method is proposed. That is, adaptive weights are assigned to corresponding samples based on the similarity measurement, followed by a recursive updating to obtain local models. This AL W-KR framework is applied to the prediction of biomass concentration in the penicillin fed-batch process. The experimental results show that the proposed ALW-KR model could predict the biomass concentration more accurate and robust to batch-to-batch variation than traditional KR methods. ©2010 IEEE.

关键词： Regression analysis

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Probabilistic process monitoring with Bayesian regularization

Probabilistic process monitoring with Bayesian regularizatio...

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作者： Zhang, Muguang Ge, Zhiqiang Song, Zhihuan State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 Zhejiang China

ISBN: (纸本)9781424474264

To monitor industrial processes through a probabilistic manner, the probabilistic principal component analysis (PPCA) method has recently been introduced. However, PPCA has its inherent limitation that it cannot determine the effective dimensionality of latent variables. This paper intends to introduce a Bayesian treatment upon the traditional principal component analysis method for process monitoring, which can automatically determine the effective number of retained principal components. Thus, a Bayesian principal component analysis based monitoring approach is developed. A case study of the Tennessee Eastman (TE) benchmark process shows the feasibility and efficiency of the proposed method. © 2010 AACC.

关键词： process monitoring

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A nussbaum technique for unknown time-varying control direction with switching sign

A nussbaum technique for unknown time-varying control direct...

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作者： Yang, Zaiyue State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University China Dept. of Applied Mathematics Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9787894631046

In this paper, a robust control approach is proposed for a class of nonlinear systems that contain both nonlinear dynamics uncertainty and an unknown time-varying control direction, which is the multiplier of the control term. In particular, the unknown control direction is allowed to switch its sign for an unlimited number of times. A new Nussbaum gain is designed and integrated with robust controller to tackle the sign-switching unknown control direction. It is proven that the proposed control approach can yield asymptotic convergence and guarantee the boundedness of the closed-loop signals.

关键词： Robust control

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A Method of Parameters Selecting in Robust control Based on PSO Algorithm

A Method of Parameters Selecting in Robust Control Based on ...

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The 2011 International Conference on key Engineering Materials and Computer Science(KEMCS 2011)

作者： Hua Li Zhicheng Xu Shuqing Wang Changzhou Institute of Mechatronic Technology National Laboratory of Industrial Control Technology Institute of Advanced Process ControlZhejiang University

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paperBased on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization(PSO) is used for solving the problem, and the corresponding robust parameters are obtainedSimulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

关键词： particle swarm optimization(PSO),robust control,LQR

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Congestion avoidance, detection and alleviation in wireless sensor networks

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第1期11卷 63-73页

作者： Wei-wei FANG Ji-ming CHEN Lei SHU Tian-shu CHU De-pei QIAN Sino-German Joint Software Institute School of Computer Science Beihang University Beijing 100191 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 31002 7 China Digital Enterprise Research Institute National University of Ireland Galway Ireland

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

关键词： Wireless sensor network （WSN） Congestion control Correlation Traffic multiplexing Rate regulation

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Coupling Dynamic Model of Chatter for Cold Rolling

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Journal of Iron and Steel Research International 2010年第12期17卷 30-34页

作者： YANG Xu TONG Chao-nan YUE Guang-feng MENG Jian-ji School of Information Engineering University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology- Beijing Beijing 100083 China Central Research Institute of Building and Construction Co Ltd MCC Group Beijing 100088 China

In order to build high accuracy integral dynamic models of cold rolling mill system, by analyzing the vibration process of cold rolling, the dynamic model of 4-h mill, including the rolling process model, the mill roll stand structure model and the hydraulic servo system model is built. These three models are coupled and linearized, then the multiple input and multiple output （MIMO） linear transfer function model of single stand 4-h cold mill system is obtained. The model with the proposed data proves its validity, meanwhile the effects of different working conditions on the stability of cold rolling mill system have been discussed. Simulation resulsts show that the model accords with former models and has its own advancement. It contributes to the further study and supression of coupling vibraiton.

关键词： cold rolling chatter dynamic modeling coupling simulation

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Adaptive Local Weighted Kernel-based Regression for Online Modeling of Batch processes

Adaptive Local Weighted Kernel-based Regression for Online M...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Haiqing Wang Zhihuan Song Kun Chen Jun Ji Yi Liu State Key Laboratory of Industrial Control Technology Institute of Industrial Process ControlZhejia Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou P

Fed-batch processes are inherently more difficult to characterize than continuous processes due to the variations under different operation stages, drifting and small-sample condition. The classical kernel-based regression (KR) methods, e.g., least squares support vector regression (LSSVR), aim to achieve a universal generalization performance, which may fail in some local regions when applied to batch process modeling. Local LSSVR model which only uses the neighbors of the query instance helps improve the accuracy, but it generally leads to a heavy computation load. Inspired by the idea of universal and local learning simultaneously, an adaptive local weighted kernel-based regression (ALWKR) method is proposed. That is. adaptive weights are assigned to corresponding samples based on the similarity measurement, followed by a recursive updating to obtain local models. This ALW-KR framework is applied to the prediction of biomass concentration in the penicillin fed-batch process. The experimental results show that the proposed ALWKR model could predict the biomass concentration more accurate and robust to batch-to-batch variation than traditional KR methods.

关键词： kernel-based regression adaptive local weight similarity index recursive updating fed-atch process modeling

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On parameter identifiability of linear multivariable systems with communication access constraints

On parameter identifiability of linear multivariable systems...

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World Congress on Intelligent control and Automation (WCICA)

作者： Lijuan Wang Hui Zhang National Key Laboratory of Industrial Control Technology Department of Control Science and Engineering Institute of Industrial Process Control University of Zhejiang Hangzhou Zhejiang China

The problem of on-line parameter identification was discussed for linear multivariable discrete time stochastic systems with communication access constraints. Based on the concept of parameter estimability defined with mutual information, a condition for identifiability was proved under the assumption that the time-varying parameter can be modeled as a Gauss-Markov process, and the sensors access status is described by binary-value function. Analytical analysis and simulation results show that, there is a proper communication strategy which preserving the identifiability of the system under access constraints.

关键词： Mutual information MIMO Kalman filters Stochastic systems Laboratories Simulation industrial control

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