Chong Lin

Qingdao University, Tsingtao, Shandong Sheng, China

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Publications (102)159.8 Total impact

  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: This paper is concerned with the problem of adaptive fuzzy output tracking control for a class of nonlinear pure-feedback stochastic systems with unknown dead-zone. Fuzzy logic systems in Mamdani type are used to approximate the unknown nonlinearities, then a novel adaptive fuzzy tracking controller is designed by using backstepping technique. The control scheme is systematically derived without requiring any information on the boundedness of dead-zone parameters (slopes and break-points) and the repeated differentiation of the virtual control signals. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighbourhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.
    International Journal of Systems Science 12/2014; 45(12). · 1.31 Impact Factor
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    ABSTRACT: This paper investigates the problem of adaptive neural control design for a class of single-input single-output strict-feedback stochastic nonlinear systems whose output is an known linear function. The radial basis function neural networks are used to approximate the nonlinearities, and adaptive backstepping technique is employed to construct controllers. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. The salient property of the proposed scheme is that only one adaptive parameter is needed to be tuned online. So, the computational burden is considerably alleviated. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.
    International Journal of Robust and Nonlinear Control 05/2014; 24(7). · 1.90 Impact Factor
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: In this paper, an adaptive fuzzy decentralized control approach is proposed for a class of uncertain stochastic nonlinear large-scale systems. Fuzzy logic systems are used to approximate the unknown nonlinearities and backstepping technique is utilized to construct adaptive fuzzy decentralized controller. It is shown that the proposed control scheme guarantees that all the closed-loop systems are semi-globally uniformly ultimately bounded in probability. Compared with the existing adaptive fuzzy decentralized control approaches, the proposed controller is simpler, and only one adaptive parameter needs to be estimated online for each subsystem. A numerical example is provided to illustrate the effectiveness of the suggested approach.
    Neurocomputing 04/2014; 103:155–163. · 1.63 Impact Factor
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    ABSTRACT: In this paper, the problem of the adaptive neural control is considered for a class of pure-feedback stochastic nonlinear systems. Based on the radial basis function (RBF) neural networks' universal approximation property, an adaptive neural controller is developed via backstepping technique. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are bounded in the sense of mean quartic value. Compared with the existing results on adaptive control of stochastic pure-feedback nonlinear systems, the main novelty of this note is that a systematic design procedure is presented for a class of pure-feedback stochastic nonlinear systems with a more general form of the diffusion term. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.
    Neurocomputing 01/2014; 135:348–356. · 1.63 Impact Factor
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    ABSTRACT: In this paper, the problem of adaptive neural tracking control is considered for a class of single-input/single-output (SISO) strict-feedback stochastic nonlinear systems with input saturation. To deal with the non-smooth input saturation nonlinearity, a smooth nonaffine function of the control input signal is used to approximate the input saturation function. Classical adaptive technique and backstepping are used for control synthesis. Based on the mean-value theorem, a novel adaptive neural control scheme is systematically derived without requiring the prior knowledge of bound of input saturation. It is shown that under the action of the proposed adaptive controller all the signals of the closed-loop system remain bounded in probability and the tracking error converges to a small neighborhood around the origin in the sense of mean quartic value. Two simulation examples are provided to demonstrate the effectiveness of the presented results.
    Information Sciences. 01/2014; 269:300–315.
  • Zhou-Yang Liu, Chong Lin, Bing Chen
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    ABSTRACT: This paper is concerned with the problem of delay-dependent stability for a class of singular time-delay systems. By representing the singular system as a neutral form, using an augmented Lyapunov-Krasovskii functional and the Wirtinger-based integral inequality method, we obtain a new stability criterion in terms of a linear matrix inequality (LMI). The criterion is applicable for the stability test of both singular time-delay systems and neutral systems with constant time delays. Illustrative examples show the effectiveness and merits of the method.
    Journal of the Franklin Institute 01/2014; · 2.42 Impact Factor
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure-feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed-loop system are semi-globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright © 2012 John Wiley & Sons, Ltd.
    International Journal of Adaptive Control and Signal Processing 08/2013; 27(4):302-322. · 1.22 Impact Factor
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    ABSTRACT: This paper focuses on approximation-based adaptive neural control of a class of nonlinear non-strict-feedback systems. Based on the structural characteristic and the monotonously increasing property of the system bounding functions, a variable separation method is first developed. By this method, an approximation-based adaptive backstepping approach is proposed for a class of nonlinear non-strict-feedback systems. It is shown that the proposed controller guarantees semi-global boundedness of all the signals in the closed-loop systems. Three examples are used to illustrate the effectiveness of the proposed approach.
    IEEE transactions on cybernetics. 07/2013;
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: This paper considers the problem of adaptive neural decentralized control for pure-feedback nonlinear interconnected large-scale systems. Radical basis function (RBF) neural networks are used to model packaged unknown nonlinearities and backstepping is used to construct decentralized controller. The proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. A numerical example is provided to illustrate the effectiveness of the suggested approach.
    Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II; 07/2013
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    ABSTRACT: This paper focuses on the problem of adaptive fuzzy tracking control for a class of nonlinear multi-input and multi-output (MIMO) time-delay systems in strict-feedback form. The time delays in the systems may be time-varying. Based on Razumikhin functional method, the state feedback adaptive fuzzy tracking controllers are constructed via backstepping technique. The proposed adaptive fuzzy controllers guarantee that all the signals in the closed-loop system are bounded and the system's outputs converge to a small neighborhood of the reference signals. Three examples are used to illustrate the effectiveness of the proposed approach.
    Fuzzy Sets and Systems 04/2013; 217:1–21. · 1.75 Impact Factor
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    ABSTRACT: This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.
    IEEE transactions on cybernetics. 02/2013;
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    ABSTRACT: This paper focuses on the problem of adaptive control for a class of nonlinear multi-input and multi-output (MIMO) systems with time delays. A state feedback adaptive controller is constructed by backstepping technique. Fuzzy logic systems are used to approximate unknown nonlinear functions in the process of the controller design. The main advantages of the proposed approach are twofold: (1) the controller design is independent of the knowledge of the basis functions of fuzzy logic systems, and (2) the suggested approach requires only m adaptive laws to control a nonlinear time-delay system with m inputs. Simulation results illustrate the effectiveness of the proposed approach.
    Information Sciences. 02/2013; 222:576–592.
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: This paper considers the problem of fuzzy-based direct adaptive tracking control for pure-feedback stochastic nonlinear systems. Fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighborhood around the origin in the sense of mean quartic value. The main advantages lie in that the proposed controller is simpler and only one adaptive parameter need to be updated online. Simulation example further illustrate the effectiveness of the proposed approach.
    Control and Decision Conference (CCDC), 2013 25th Chinese; 01/2013
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: This paper focuses on the problem of the adaptive neural control for a class of a perturbed pure-feedback nonlinear system. Based on radial basis function (RBF) neural networks’ universal approximation capability, an adaptive neural controller is developed via the backstepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error eventually converges to a small neighborhood around the origin. The main advantage of this note lies in that a control strategy is presented for a class of pure-feedback nonlinear systems with external disturbances being bounded by functions of all state variables. A numerical example is provided to illustrate the effectiveness of the suggested approach.
    Nonlinear Dynamics 01/2013; 72(1-2). · 3.01 Impact Factor
  • Ya-Kun Su, Bing Chen, Qi Zhou, Chong Lin
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    ABSTRACT: This article deals with the problem of H ∞ filter design for nonlinear discrete-time systems with norm-bounded parameter uncertainties and time-varying delays. A new Lyapunov function and free-weighting matrix method are used for filtering design, consequently, a delay-dependent design method is first proposed in terms of linear matrix inequalities, which produces a less conservative result. Finally, numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method.
    International Journal of Systems Science 08/2012; 43(8):1568-1579. · 1.31 Impact Factor
  • Huanqing Wang, Bing Chen, Chong Lin
    Nonlinear Dynamics 02/2012; · 3.01 Impact Factor
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: The problem of robust stabilization is investigated for strict-feedback stochastic nonlinear time-delay systems via adaptive neural network approach. Neural networks are used to model the unknown packaged functions, then the adaptive neural control law is constructed by a novel Lyapunov–Krasovskii functional and backstepping. It is shown that all the variables in the closed-loop system are semi-globally stochastic bounded, and the state variables converge into a small neighborhood in the sense of probability.
    Nonlinear Dynamics 01/2012; 77:267-274. · 3.01 Impact Factor
  • Bing Chen, X.P. Liu, S.S. Ge, Chong Lin
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    ABSTRACT: Controlling nonstrict-feedback nonlinear systems is a challenging problem in control theory. In this paper, we consider adaptive fuzzy control for a class of nonlinear systems with nonstrict-feedback structure by using fuzzy logic systems. A variable separation approach is developed to overcome the difficulty from the nonstrict-feedback structure. Furthermore, based on fuzzy approximation and backstepping techniques, a state feedback adaptive fuzzy tracking controller is proposed, which guarantees that all of the signals in the closed-loop system are bounded, while the tracking error converges to a small neighborhood of the origin. Simulation studies are included to demonstrate the effectiveness of our results.
    IEEE Transactions on Fuzzy Systems 01/2012; 20(6):1012-1021. · 5.48 Impact Factor
  • Huanqing Wang, Bing Chen, Chong Lin
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    ABSTRACT: In this paper, the problem of adaptive fuzzy control design is investigated for strict-feedback stochastic nonlinear systems. Fuzzy logic systems are used to approximate the packaged unknown functions and adaptive backstepping technique is utilized to construct adaptive controller. The proposed controller guarantees that all the signals in the closed-loop system are bounded in probability. The main advantages of the proposed controller lie in that fuzzy basis function vectors are not contained in the proposed controller, which makes the adaptive fuzzy control scheme is very simple. And the proposed controller contains only one adaptive parameter that needs to be updated online. Simulation results further demonstrate the effectiveness of the proposed approach.
    Control and Decision Conference (CCDC), 2012 24th Chinese; 01/2012
  • Tao Li, Wei Xing Zheng, Chong Lin
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    ABSTRACT: By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.
    IEEE Transactions on Neural Networks 12/2011; 22(12):2138-2143. · 2.95 Impact Factor

Publication Stats

2k Citations
159.80 Total Impact Points

Institutions

  • 2006–2014
    • Qingdao University
      • • Institute of Complexity Sciences
      • • College of Automation Engineering
      Tsingtao, Shandong Sheng, China
  • 2011
    • Nanjing University of Information Science & Technology
      Nan-ching, Jiangsu Sheng, China
  • 2009
    • Brunel University
      • Department of Information Systems and Computing
      London, ENG, United Kingdom
  • 2008
    • Bohai University
      Tsingtao, Shandong Sheng, China
    • Nanjing University
      • Department of Space Science and Technology
      Nanjing, Jiangsu Sheng, China
  • 2002–2008
    • National University of Singapore
      • Department of Electrical & Computer Engineering
      Singapore, Singapore
  • 2006–2007
    • Central South University
      • School of Information Science and Engineering
      Changsha, Hunan, China
  • 1999–2001
    • The University of Hong Kong
      • Department of Mechanical Engineering
      Hong Kong, Hong Kong
    • Nanyang Normal University
      Nan-yang-shih, Henan Sheng, China
  • 1997–1999
    • Nanyang Technological University
      • School of Electrical and Electronic Engineering
      Tumasik, Singapore