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Chun-fei Hsu - One of the best experts on this subject based on the ideXlab platform.

  • intelligent control of chaotic systems via self organizing hermite polynomial based Neural network
    Neurocomputing, 2014
    Co-Authors: Chun-fei Hsu
    Abstract:

    This paper proposes an adaptive self-organizing Hermite-polynomial-based Neural control (ASHNC) system which is composed of a Neural Controller and a supervisor compensator. The Neural Controller uses a self-organizing Hermite-polynomial-based Neural network (SHNN) to approximate an ideal feedback Controller. For the SHNN, the developed self-organizing approach is clearly and easily used for real-time systems and the parameter learning ability is effective with high convergence precision and fast convergence time. The supervisor compensator is designed to eliminate the approximation error between the Neural Controller and ideal feedback Controller without chattering phenomena. Moreover, a proportional-integral (PI) type adaptation law is derived based on the Lyapunov stability theory; thus not only the system stability of the control system can be guaranteed but also the convergence of the tracking error can be speeded up. Finally, the proposed ASHNC system is applied to a chaotic system. Simulation results demonstrate that the proposed ASHNC system can achieve favorable control performance.

  • a self evolving functional linked wavelet Neural network for control applications
    Applied Soft Computing, 2013
    Co-Authors: Chun-fei Hsu
    Abstract:

    Abstract The structure of a Neural network is determined by time-consuming trial-and-error tuning procedure in advance for the reason that it is difficult to consider the balance between the neuron number and the desired performance. To attack this problem, a self-evolving functional-linked wavelet Neural network (SFWNN) is proposed. Without the need for preliminary knowledge, a self-evolving approach demonstrates that the properties of generating and pruning the hidden neurons automatically. Then, an adaptive self-evolving functional-linked wavelet Neural control (ASFWNC) system which is composed of a Neural Controller and a supervisory compensator is proposed. The Neural Controller uses a SFWNN to online estimate an ideal Controller and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the Neural Controller upon the system stability in the Lyapunov sense. To investigate the capabilities of the proposed ASFWNC approach, it is applied to a chaotic system and a DC motor. The simulation and experimental results show that favorable control performance can be achieved by the proposed ASFWNC scheme.

  • adaptive Neural complementary sliding mode control via functional linked wavelet Neural network
    Engineering Applications of Artificial Intelligence, 2013
    Co-Authors: Chun-fei Hsu
    Abstract:

    Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive Neural complementary sliding-mode control (ANCSC) system, which is composed of a Neural Controller and a robust compensator, for a chaotic system. The Neural Controller uses a functional-linked wavelet Neural network (FWNN) to approximate an ideal complementary sliding-mode Controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the Neural Controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the Controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.

  • Design of a CMAC-based smooth adaptive Neural Controller with a saturation compensator
    Neural Computing and Applications, 2012
    Co-Authors: Ming-ching Yen, Chun-fei Hsu, In-hang Chung
    Abstract:

    In the conventional CMAC-based adaptive Controller design, a switching compensator is designed to guarantee system stability in the Lyapunov stability sense but the undesirable chattering phenomenon occurs. This paper proposes a CMAC-based smooth adaptive Neural control (CSANC) system that is composed of a Neural Controller and a saturation compensator. The Neural Controller uses a CMAC Neural network to online mimic an ideal Controller and the saturation compensator is designed to dispel the approximation error between the ideal Controller and Neural Controller without any chattering phenomena. The parameter adaptive algorithms of the CSANC system are derived in the sense of Lyapunov stability, so the system stability can be guaranteed. Finally, the proposed CSANC system is applied to a Chua’s chaotic circuit and a DC motor driver. Simulation and experimental results show the CSANC system can achieve a favorable tracking performance. It should be emphasized that the development of the proposed CSANC system doesn’t need the knowledge of the system dynamics.

  • Adaptive dynamic RBF Neural Controller design for a class of nonlinear systems
    Applied Soft Computing, 2011
    Co-Authors: Chun-fei Hsu
    Abstract:

    In this paper, an adaptive DRBF Neural control (ADNC) system which is composed of a Neural Controller and a smooth compensator is proposed. The Neural Controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal Controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal Controller and Neural Controller. The DRBF network can self-organizing its network structure. All the Controller parameters of the proposed ADNC system are online tuned in the Lyapunov sense, thus the stability analytic shows the system output can exponentially converge to a small neighborhood of the trajectory command. Finally, the proposed ADNC system is applied to a chaotic system and a DC motor. Simulation and experimental results verify that a favorable tracking performance and no chattering phenomena can be achieved by the proposed ADNC system.

Michael Kuperstein - One of the best experts on this subject based on the ideXlab platform.

  • INFANT Neural Controller for adaptive sensory-motor coordination
    Neural Networks, 1991
    Co-Authors: Michael Kuperstein
    Abstract:

    Abstract This review presents a theory and prototype for a Neural Controller called INFANT that learns sensory-motor coordination from its own experience. Three adaptive abilities are discussed: locating stationary targets with movable sensors; grasping arbitrarily positioned and oriented targets in 3D space with multijoint arms, and positioning an unforeseen payload with accurate and stable movements despite unknown sensor feedback delay. INFANT adapts to unforeseen changes in the geometry of the physical motor system, the internal dynamics of the control circuits and to the location, orientation, shape, weight, and size of objects. It learns to accurately grasp an elongated object with almost no information about the geometry of the physical sensory-motor system. This Neural Controller relies on the self-consistency between sensory and motor signals to achieve unsupervised learning. It is designed to be generalized for coordinating any number of sensory inputs with limbs of any number of joints. The principle theme of the review is how various geometries of interacting topographic Neural fields can satisfy the constraints of adaptive behavior in complete sensory-motor circuits.

Feng-yuan Chang - One of the best experts on this subject based on the ideXlab platform.

  • Universal Neural Controllers and stability analysis
    International Journal of Systems Science, 1996
    Co-Authors: Cheng-liang Chen, Feng-yuan Chang
    Abstract:

    A set Γ of Neural Controllers is a universal Neural Controller if and only if any given controllable process P can also be controlled by a specific Neural Controller in Γ. Given any continuous real-valued Controller on a compact set, there exists an element in the set Γ of PD/PI type inverse gaussian and gaussian potential function Neural Controllers (IGFNCs, GPFNCs) that could approximate the given continuous Controller to any degree of accuracy. An application of this result shows that this type of radial basis function Neural Controller (RBFNCs) is a universal Neural Controller. Hence, any given controllable process can be controlled by this type of RBFNCs operating simultaneously. To realize the universal Neural Controller in practical processes, a stable region of PD type GPFNC control system is studied in parametric space of the gaussian potential frunctton network (GPFN)

  • Universal Neural Controllers
    Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994
    Co-Authors: Cheng-liang Chen, Feng-yuan Chang
    Abstract:

    A collection /spl Gamma/ of a Neural Controller is a universal Neural Controller if and only if any given controllable process P can also be controlled by a specific Neural Controller in /spl Gamma/. Given any continuous real valued function on a compact set, we show that the collection /spl Gamma/ of the inverse Gaussian and the Gaussian potential function Neural Controllers (IGFNC, GPFNC) that will approximate the given function to any degree of accuracy. An application of this result shows that this type of radial basis function Neural Controller (RBFNC) is a universal Neural Controller. Hence, any given controllable process can be controlled by this RBFNC operating simultaneously.

Weiyen Wang - One of the best experts on this subject based on the ideXlab platform.

  • dynamic slip ratio estimation and control of antilock braking systems using an observer based direct adaptive fuzzy Neural Controller
    IEEE Transactions on Industrial Electronics, 2009
    Co-Authors: Weiyen Wang, Mingchang Chen, Shiboun Hsu
    Abstract:

    This paper proposes an antilock braking system (ABS), in which unknown road characteristics are resolved by a road estimator. This estimator is based on the LuGre friction model with a road condition parameter and can transmit a reference slip ratio to a slip-ratio Controller through a mapping function. The slip-ratio Controller is used to maintain the slip ratio of the wheel at the reference values for various road surfaces. In the Controller design, an observer-based direct adaptive fuzzy-Neural Controller (DAFC) for an ABS is developed to online-tune the weighting factors of the Controller under the assumption that only the wheel slip ratio is available. Finally, this paper gives simulation results of an ABS with the road estimator and the DAFC, which are shown to provide good effectiveness under varying road conditions.

  • an online ga based output feedback direct adaptive fuzzy Neural Controller for uncertain nonlinear systems
    Systems Man and Cybernetics, 2004
    Co-Authors: Weiyen Wang, Chihyuan Cheng, Yihguang Leu
    Abstract:

    In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-Neural Controller (GODAF Controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-Neural Controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-Neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-Neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-Neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-Neural Controller is established by the Lyapunov design approach. A supervisory Controller is incorporated into the GODAF Controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF Controller are demonstrated to illustrate the effectiveness of the proposed method.

  • observer based adaptive fuzzy Neural control for unknown nonlinear dynamical systems
    Systems Man and Cybernetics, 1999
    Co-Authors: Yihguang Leu, Tsutian Lee, Weiyen Wang
    Abstract:

    In this paper, an observer-based adaptive fuzzy-Neural Controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-Neural Controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

  • Robust adaptive fuzzy-Neural Controllers for uncertain nonlinear systems
    IEEE Transactions on Robotics and Automation, 1999
    Co-Authors: Yih-guan Leu, Weiyen Wang, Tsutian Lee
    Abstract:

    A robust adaptive fuzzy-Neural Controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed. The fuzzy-Neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses a fixed number of control points is developed for online tuning. The concept of tuning the adjustable vectors, which include membership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-Neural Controller. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by the error compensator which is also constructed by fuzzy-Neural inference. We prove that the closed-loop system which is controlled by the robust adaptive fuzzy-Neural Controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods.

Cheng-liang Chen - One of the best experts on this subject based on the ideXlab platform.

  • Universal Neural Controllers and stability analysis
    International Journal of Systems Science, 1996
    Co-Authors: Cheng-liang Chen, Feng-yuan Chang
    Abstract:

    A set Γ of Neural Controllers is a universal Neural Controller if and only if any given controllable process P can also be controlled by a specific Neural Controller in Γ. Given any continuous real-valued Controller on a compact set, there exists an element in the set Γ of PD/PI type inverse gaussian and gaussian potential function Neural Controllers (IGFNCs, GPFNCs) that could approximate the given continuous Controller to any degree of accuracy. An application of this result shows that this type of radial basis function Neural Controller (RBFNCs) is a universal Neural Controller. Hence, any given controllable process can be controlled by this type of RBFNCs operating simultaneously. To realize the universal Neural Controller in practical processes, a stable region of PD type GPFNC control system is studied in parametric space of the gaussian potential frunctton network (GPFN)

  • Universal Neural Controllers
    Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994
    Co-Authors: Cheng-liang Chen, Feng-yuan Chang
    Abstract:

    A collection /spl Gamma/ of a Neural Controller is a universal Neural Controller if and only if any given controllable process P can also be controlled by a specific Neural Controller in /spl Gamma/. Given any continuous real valued function on a compact set, we show that the collection /spl Gamma/ of the inverse Gaussian and the Gaussian potential function Neural Controllers (IGFNC, GPFNC) that will approximate the given function to any degree of accuracy. An application of this result shows that this type of radial basis function Neural Controller (RBFNC) is a universal Neural Controller. Hence, any given controllable process can be controlled by this RBFNC operating simultaneously.