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IEEE International Symposium on Intelligent control (ISIC)

作者： Xuhua Yang Huaping Dai Guojiang Shen Youxian Sun National Laboratory of Industrial Control Technology Institute of Industrial Process Control University of Zhejiang Hangzhou China

On the base of the Fourier neural networks, this paper adopted dichotomy to search the neural networks' optimization structure and optimization learning rate. Given the variational ranges of the Fourier neural networks' structure and learning rate, on the condition of arbitrary nonlinear mapping relationship, arbitrary error request and arbitrary training sample number, this algorithm can adjust the fourier neural networks' structure and learning rate automatically to the optimization structure and the optimization learning rate. The simulation results showed that the convergence speed of the fourier neural networks can be greatly improved if the fourier neural networks adopt the optimization structure and the optimization learning rate.

关键词： Neural networks Fourier series Convergence Optimization methods industrial control Sun Laboratories industrial training process control Error correction

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Model-free PID controller with gain scheduling for turning processes

Model-free PID controller with gain scheduling for turning p...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ning Wang Zhong Zheng Honglu Chen National Key Laboratory of Industrial Control Technology And Research Inst. of Advanced Process Control Zhejiang University of Technology Hangzhou China National Key Lab. of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)0780379527

The model-free PID control method with fuzzy neuron gain scheduling is proposed for turning processes in this paper. In order to enhance the control system stability and adaptability to the plants with nonlinearities and uncertainties, the model-free PID controller is designed to keep the cutting force to be constant by changing the controller gain on-line when a cutting tool cuts at various cutting depth or the spindle operates in different speeds. In this control system, the PID controller is used to control the turning process, the neuron is applied to tune the PID controller gain, and a fuzzy scheme is set up to change the neuron gain. With the two examples of different turning processes, the experiments of using the proposed control method are made. The simulation results show the good performance of the model-free PID controller.

关键词： Three-term control Turning control systems Neurons Force control Nonlinear control systems control nonlinearities Fuzzy control control system synthesis Stability

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Neuron double-layer control

Neuron double-layer control

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ning Wang National Key Laboratory of Industrial Control Technology And Research Institute of Advanced Process Contro1 University of Zhejiang Hangzhou China

ISBN: (纸本)0780378652

The neuron PID double-layer control system is set up and the double-layer control method is proposed for control system with uncertainties in this paper. In this control system, a conventional PID controller is used in the insider layer control loop, and the neuron model-free controller is used in the outsider layer control loop. As a result of utilizing the double layer control structure, the PID control parameters can be selected imprecisely and the control system dynamics can be improved efficiently. The application of the proposed control method to the liquid level control of a tower of the fluid catalytic cracking unit (FCCU) is described. The excellent performance obtained in practice shows that the new control method has powerful vitality in industrial control.

关键词： Neurons control systems Three-term control Uncertainty Machine learning industrial control Level control Poles and towers control system synthesis Hebbian theory

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An intelligent controller based on two neurons

An intelligent controller based on two neurons

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ning Wang Jian-Hua Wu National Key Laboratory of Industrial Control Technology And Research Institute of Advanced Process Control University of Zhejiang Hangzhou China College of Electrical Engineering University of Zhejiang Hangzhou China

ISBN: (纸本)0780378652

According to the neuron model and its learning strategy in [Wang Ning et al., 1991], the intelligent control method based on two neurons is presented in this paper. In this control system, the two neurons are used to structure the compound controller. One neuron acts as a feed-forward and PD controller, the other neuron acts as a PI controller. With an example of basis weight control, the simulation tests are made to demonstrate the effectiveness of the proposed controller in handing plants with uncertainties.

关键词： Neurons control systems Intelligent control Uncertainty Weight control industrial control process control Paper making machines Three-term control Electrical equipment industry

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Nonlinear neuron control of a direct drive robot

Nonlinear neuron control of a direct drive robot

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ning Wang Jianhua Wu National Key Laboratory of Industrial Control Technology And Research Inst. of Advanced Process Control Zhejiang University of Technology Hangzhou China College of Electrical Engineering Zhejiang University of Technology Hangzhou China

ISBN: (纸本)0780379527

The nonlinear neuron control method is proposed for a direct drive robot in this paper. In this control system, the neuron controller and the relay controller are designed in parallel connection to form the model-free controller to control a direct drive robot with uncertainties. A fuzzy scheme is used to tune the relay controller gain on line. With an example of a direct drive robot manipulator, the experiments are made. The simulation results demonstrate that the proposed method can efficiently control the independent joint of the direct drive robot. This model-free controller has excellent performance, strong robustness and adaptability.

关键词： Neurons Nonlinear control systems control systems Sliding mode control Manipulator dynamics Service robots Pulse width modulation Uncertainty Fuzzy control Robot control

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Normalized cascade structures and state feedback control logic synthesis

Normalized cascade structures and state feedback control log...

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IEEE Conference on Decision and control

作者： Lida Dong Weimin Wu Hongye Su Jian Chu Institute of Advanced National Laboratory of Industrial Control Technology Institute of Electronic Circuit and Information System University of Zhejiang Hangzhou China

ISBN: (纸本)0780379241

This paper proposed a new state feedback controller synthesis method for solving forbidden states avoidance problems. The method can be used when all influentially uncontrollable subnets have normalized cascade structures, which differ from marked graph, state machine as well as forward and backward conflict-free structure. Two advantages of the method are shown as follows: 1) It is suitable for a class of controlled discrete event plants modeled by generalized Petri nets; 2) The online computation of maximally permissive control law can be carried out within polynomial times.

关键词： State feedback Petri nets Circuit synthesis control system synthesis Monitoring Polynomials Logic circuits Laboratories industrial control process control

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control synthesis for a class of controlled Petri nets 15th

Control synthesis for a class of controlled Petri nets

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15th World Congress of the International Federation of Automatic control, 2002

作者： Wu, Weimin Su, Hongye Chu, Jian Zhai, Haifeng National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9783902661746

This paper proposes a new control synthesis method for a class of discrete event systems modeled by controlled ordinary Petri nets with linear marking constraint. Monitor is constructed to track the system state resulted from the uncontrollable firing sequences. The maximally permissive feedback control policy then can be obtained based on the making of the monitor. No non-convex constraint transformation is introduced in the design procedure. The method is capable of synthesizing a class of net that cannot be treated using previous methods due to some necessary restrictions. Copyright © 2002 IFAC.

关键词： control system synthesis

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Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2002年第4期3卷 47-51页

作者：张克勤庄开宇苏宏业褚健高红 National Laboratory of Industrial Control Technology Institute of Advanced Process ControlZhejiang UniversityHangzhouChina State Key Laboratory of Fluid Power Transmission and ControlZhejiang UniversityHangzhouChina

This paper presents a sliding mode (SM) based identifier to deal with the parameter identification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system; an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.

关键词： Nonlinear system Sliding mode Identifier Input nonlinearity

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Model-free control based on neural networks

Model-free control based on neural networks

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhong Zheng Ning Wang National Key Laboratory of Industrial Control Technology and Research Institute of Advanced Process Control University of Zhejiang China

ISBN: (纸本)0780375084

A model-free control method for nonlinear plants is proposed. According to the neuron model and learning strategy in Wang et al. (1991), the neural network model is structured and the learning algorithm is also presented. Based on the neural network, the model-free controller is designed. In an example of control of a pH process, the simulation results show that the proposed control method can control a nonlinear plant efficiently.

关键词： Neural networks Neurons Machine learning Artificial neural networks control systems Laboratories industrial control Paper technology process control Uncertainty

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control SYNTHESIS FOR A CLASS OF controlLED PETRI NETS

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IFAC Proceedings Volumes 2002年第1期35卷 229-234页

作者： Weimin Wu Hongye Su Jian Chu Haifeng Zhai National Laboratory of Industrial Control Technology & Institute of Advanced Process Control Zhejiang University Hangzhou 310027 P.R.China

关键词： discrete event systems Petri nets control system synthesis constraint monitor

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