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Chinese control Conference

作者： Tianrui Chen David J. Hill Cong Wang School of Automation Guangdong University of Technology Guangzhou P. R. China Department Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation and the Center for Control and Optimization South China University of Technology Guangzhou P. R. China

In this paper, a fault diagnosis approach is proposed for power systems based on deterministic learning (DL) theory. First, a learning estimator is constructed to obtain knowledge of the fault dynamics in the power networks. Based on DL, local accurate approximations of the unknown fault functions are achieved. Second, a distributed fault diagnosis scheme is designed to monitor the power systems. The knowledge of the fault dynamics is used to detect the occurrence of a fault. Simulation studies are included to demonstrate the effectiveness of the approach.

关键词： Fault diagnosis Power system dynamics Trajectory Circuit faults Radial basis function networks Monitoring

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Rapid Oscillation Fault Detection for Distributed System via Deterministic Learning

Rapid Oscillation Fault Detection for Distributed System via...

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第25届中国控制与决策会议

作者： Tianrui Chen Cong Wang David J. Hill School of Automation Guangdong University of Technology School of Electrical and Information Engineering University of Sydney School of Automation and the Center for Control and Optimization South China University of Technology

ISBN: (纸本)9781467355339

In this paper, a rapid detection and isolation scheme for oscillation faults in a distributed nonlinear system is proposed based on a recent result on deterministic learning (DL) theory. The distributed nonlinear system considered is modeled as a set of interconnected subsystems. Firstly, a local learning and merging method based on DL is proposed to obtain knowledge of the unknown interconnections and the fault functions. Secondly, by utilizing learned knowledge, a bank of consensus-based dynamical estimators are constructed for each subsystem, and average 1 norms of the residuals are generated to make the detection and isolation decisions. Thirdly, a rigorous analysis for characterizing the detection and isolation capabilities of the proposed scheme is given. The attraction of the intelligence fault diagnosis approach is to give a fast response to faults by using the learned knowledge and processing huge data in a dynamical and distributed manner. Simulation studies are included to demonstrate the effectiveness of the approach.

关键词： Fault detection and isolation, distributed systems, deterministic learning, radial basis function neural networks, persistent excitation condition.

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Detection of Rotating Stall Based on Deterministic Learning

Detection of Rotating Stall Based on Deterministic Learning

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第三十二届中国控制会议

作者： Wenjie Si Cong Wang Binhe Wen Yong Wang Anping Hou School of Automation and Center for Control and Optimization South China University of Technology Department of Mechanics and Engineering Science Peking University School of Energy and Power Engineering BeiHang University

ISBN: (纸本)9781479900305

In this paper,deterministic learning theory is used to detect the stall inception signal for the axial ***, based on deterministic learning(DL) theory,the system dynamics underlying normal and stall inception signal are identified and stored in constant radial basis function(RBF) ***,through the method of dynamic pattern recognition in DL, the stall inception of the axial compressor could be *** results show the validity of the proposed approach.

关键词： Deterministic learning Rotating stall Identification Dynamic pattern recognition Rapid detection

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Small-Gain Stability Conditions for Linear Systems with Time-Varying Delays

Small-Gain Stability Conditions for Linear Systems with Time...

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American control Conference

作者： Jing Zhu Jie Chen Tian Qi Department of Electronic Engineering City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong SAR Center for Control and Optimization School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781479901777

Sufficient stability conditions for linear systems subject to time-varying delays are developed in this paper, which are in the form of scaled small gain conditions and can be checked readily by solving corresponding linear matrix inequality problems. From a robust control perspective, our development seeks to cast the stability problem as one of robust stability analysis, and the resulting stability conditions are also reminiscent of robust stability bounds typically found in robust control theory. Examples show significant improvement of our results over the existing criteria.

关键词： Linear system system clock time varying Robust stability stability problems linear matrix inequalities Robust control theory Stability analysis

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Multi-Agent Consensus under Delayed Feedback: Fundamental Constraint on Graph and Fundamental Bound on Delay

Multi-Agent Consensus under Delayed Feedback: Fundamental Co...

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American control Conference

作者： Tian Qi Li Qiu Jie Chen Center for Control and Optimization School of Automation Science and Engineering South China University of Technology Guangzhou China work performed while at Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781479901777

In this paper, we study the consensus problem for discrete-time multi-agent systems over an undirected, fixed network communication graph. We assume that the agents' input is subject to a constant albeit possibly unknown time delay, and employ a distributed dynamic state feedback control protocol. Drawing upon concepts and techniques from robust control, notably those concerning gain margin optimization and analytic interpolation, we derive explicit, closed-form conditions for general linear agents to achieve consensus. Our results display an explicit dependence of the consensus condition on the agent's unstable dynamics and the delay value, showing that delayed communication between agents will generally hinder consensus and impose restrictions on the network topology. We also show that a lower bound on the maximal delay allowable for consensus can be computed by a simple line search method.

关键词： agents network maximal Agents Maximal delay Line graph Network topology Delayed Feedback Explicit

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Deterministic learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems

Deterministic learning from ISS-modular adaptive NN control ...

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2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems, CIS 2011

作者： Wang, Cong Wang, Min School of Automation and Center for Control and Optimization South China University of Technology Guangzhou 510641 China

ISBN: (纸本)9781612841984

This paper studies deterministic learning from adaptive neural control for a class of strict-feedback nonlinear systems with unknown affine terms. Firstly, an ISS-modular approach is presented to ensure uniformly ultimate boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. The proposed ISS-modular approach avoids the possible control singularity without the restriction of the derivative of affine terms. Secondly, it will be shown the proposed stable ISS-modular adaptive neural controller is able to learn closed-loop system dynamics. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using previous methods. To overcome these difficulties, the stable closed-loop system in control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation (PE) condition for radial basis function (RBF) neural networks (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop control system dynamics is achieved in a local region along a recurrent orbit of closed-loop signals, and a learning ability is implemented during a closed-loop feedback control process. The learned knowledge can be reused in the same or similar control tasks, and avoid the tremendous repeated training process of NNs. © 2011 IEEE.

关键词： Closed loop control systems

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Deterministic learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems

Deterministic learning from ISS-modular adaptive NN control ...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Cong Wang Min Wang School of Automation and Center for Control and Optimization South China University of Technology Guangzhou China

关键词： Artificial neural networks Adaptive systems Radial basis function networks Approximation methods Process control Stability analysis Orbits

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State observation for a class of uncertain nonlinear systems via deterministic learning

State observation for a class of uncertain nonlinear systems...

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作者： Zhou, Guopeng Wang, Cong College of Mathematics and statistics Xianning University Xianning 437100 China School of Automation and Center for Control and Optimization South China University of Technology Guangzhou 510641 China College of Electric and Electronic Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9787894631046

In this paper, based on the recently proposed theory of deterministic learning, a new neural observer is proposed for a class of uncertain nonlinear systems undergoing periodic or recurrent motions. Firstly, it is shown that the state observation error converges to a small neighborhood of zero exponentially in finite time, and a partial persistent excitation (PE) condition is satisfied. Secondly, by imposing an auxiliary filter and constructing a new Lyapunov function, it is obtained that the neural weight estimation error also converges to a small neighborhood of zero. Thus, the system uncertain dynamics along the periodic trajectory can be locally-accurately identified by the radial basis function(RBF) neural networks(NNs) and then stored in a constant RBF NNs. Finally, a constant NNs observer is also implemented, with guaranteed stability and good performance. The proposed observer scheme does not require high-gain design, and the embedded localized NNs can learn (identify) the nonlinear uncertain dynamics along the estimated periodic or recurrent system trajectory.

关键词： Lyapunov functions

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State observation for a class of uncertain nonlinear systems via deterministic learning

State observation for a class of uncertain nonlinear systems...

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Chinese control Conference (CCC)

作者： Guopeng Zhou Cong Wang College of electric and electronic engineering Huazhong University of Science and Technology Wuhan P.R. China College of Mathematics and statistics Xianning University Xianning P.R. China School of Automation Center for Control and Optimization South China University of Technology Guangzhou P.R. China

关键词： Observers Artificial neural networks Trajectory Nonlinear systems Approximation methods Adaptive systems Radial basis function networks

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Deterministic learning from control of nonlinear systems with disturbances

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Progress in Natural Science:Materials International 2009年第8期19卷 1011-1019页

作者： Guopeng Zhou a,b, Cong Wang a, a school of automation and center for control and optimization, South China University of Technology, Guangzhou 510640, China b College of Mathematics and Statistics, Xianning University, Xianning 437005, China School of Automation and Center for Control and Optimization South China University of Technology Guangzhou 510640 China College of Mathematics and Statistics Xianning University Xianning 437005 China

In this paper, we investigate deterministic learning in environments with disturbances. We will show that for a class of uncertain nonlinear systems with bounded disturbances, by using an appropriately designed adaptive neural controller, the disturbances are attenuated and the system output tracks a periodic orbit in finite time. As radial basis function (RBF) neural networks (NN) are employed, this leads to the satisfaction of a partial persistence of the excitation (PE) condition. By using the uniform complete observability (UCO) technique, it is analyzed that partial estimated NN weights will converge to a neighborhood of zero, with the size of the neighborhood depending on the amplitude of disturbances as well as on the control gains. Locally-accurate approximation of unknown system dynamics can still be achieved in the stable NN control process. The approximation error level is influenced by the amplitude of disturbances. The obtained knowledge of system dynamics can be reused in another control process towards stability and improved performance. Simulation studies are included to demonstrate the effectiveness of the approach.

关键词： Deterministic learning Adaptive NN control Disturbance Partial PE condition Uniform complete observability

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