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International Symposium on advanced control of industrial processes (ADCONIP)

作者： J. Chen N. Cheng J. Zhang Department of Chemical Engineering Chung Yuan Christian University Chungli Taiwan Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China National Institute for Standards and Technology Boulder CO USA

A neural based PID feedback control method for networked process control systems is presented. As there are some uncertain factors such as external disturbance, randomly delayed measurements or control demands in real networked process control systems, the proposed PID controller is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed loop systems. To demonstrate the potential applications of the proposed strategy, an example of a simulated batch reactor is provided. The proposed design method is shown to be useful and effective in dealing with network process control systems.

关键词： process control Delay Artificial neural networks control systems Stochastic processes Inductors Entropy

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A Constructive Procedure based on Dynamic Scaling for Adaptive control of Nonlinearly Parameterized Nonlinear Systems

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IFAC Proceedings Volumes 2011年第1期44卷 120-125页

作者： Xiangbin Liu Romeo Ortega Hongye Su Jian Chu Advanced Control Systems Laboratory of School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China. (Tel: 86-10-5168-4105) Laboratoire des Signaux et Systèmes CNRS–SUPELEC 91192 Gif–sur–Yvette France State Key Lab. of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P. R. China

Abstract A new framework to design immersion and invariance adaptive controllers for nonlinearly parameterized, nonlinear systems was recently proposed by the authors. The key step is the construction of a monotone mapping, via a suitable selection of a controller tuning function, which has to satisfy some integrability conditions—this translates into the need to solve a partial differential equation (PDE). In this paper this result is extended providing some answers to the questions of characterization of “monotonizable” systems and solvability of the PDE. First, adding to the design a nonlinear dynamic scaling, we obviate the need to solve the PDE. Second, for the case of factorizable nonlinearities , the following results are established. (i) It is shown that the monotonicity condition is satisfied if a linear matrix inequality is feasible. (ii) Directly verifiable involutivity conditions that ensure the solution of the PDE are presented. (iii) An explicit formula for the required tuning function is given, provided the regressor matrix satisfies some rank conditions. Hence, adding a dynamic scaling, this yields a constructive solution to the problem.

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Sensor robust fault-tolerant guaranteed design for nonlinear networked control system based on T-S model

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and technology 2011年第SUPPL. 2期35卷 206-212页

作者： Wang, Jun Li, Wei Li, Zhan-Ming College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou 730050 China Key Laboratory of Advanced Control for Industrial Process in Gansu Province Lanzhou 730050 China

In an uncertain nonlinear networked control system(NNCS) with the influence on delay and packet loss, a robust fault-tolerant guaranteed controlled sufficient condition of system, which is delay-dependent approach, is deduced by constructing an appreciate Lyapunov-Krasovskii function based on T-S fused model. control gain can be obtained via iterative optimization algorithm of solving several nonlinear matrix inequalities(NLMI). In this paper, we consider the lower bound of the time-delay and do not make the model transformation, also free matrixes in integral inequalities ensure that the useful information is not be magnified and ignored. All of these obtain a less conservative result. Finally, an example is used to illustrate the effectiveness and feasibility of the proposed approach.

关键词： Networked control systems

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

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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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