教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Adaptive bound reduced-form genetic algorithms for B-spline neural network training
(IEICE, 2004-11-01) W.-Y. Wang; C.-W. Tao; C.-G. Chang
In this paper, an adaptive bound reduced-form genetic algorithm (ABRGA) to tune the control points of B-spline neural networks is proposed. It is developed not only to search for the optimal control points but also to adaptively tune the bounds of the control points of the B-spline neural networks by enlarging the search space of the control points. To improve the searching speed of the reduced-form genetic algorithm (RGA), the ABRGA is derived, in which better bounds of control points of B-spline neural networks are determined and paralleled with the optimal control points searched. It is shown that better efficiency is obtained if the bounds of control points are adjusted properly for the RGA-based B-spline neural networks.
Design of sliding mode controllers for bilinear systems with time varying uncertainties
(IEEE Systems, Man, and Cybernetics Society, 2004-02-01) C.-W. Tao; W.-Y. Wang; M.-L. Chan
Sliding mode controllers for the bilinear systems with time varying uncertainties are developed in this paper. The bilinear coefficient matching condition which is similar to the traditional matching condition for linear system is defined for the homogeneous bilinear systems. It can be seen that the bilinear coefficient matching condition is very limited and is not generally applicable to the nonhomogeneous bilinear system. Thus, the sliding coefficient matching condition is also considered for the bilinear systems with time varying uncertainties. Then, the sufficient conditions are provided for the reaching mode of the time varying uncertain bilinear systems to be guaranteed by the designed sliding mode controllers. Moreover, the stability of the uncertain bilinear systems with the sliding mode controller is discussed. Simulation results are included to illustrate the effectiveness of the proposed sliding mode controllers.
Fuzzy-neural function approximation using a vector evaluation genetic algorithm
(2003-01-01) W.-Y. Wang; C.-C. Hsu; C.-W. Tao; Y.-H. Li
A GA-based indirect adaptive fuzzy-neural controller for uncertain nonlinear systems
(2002-12-06) W.-Y. Wang; C.-C. Hsu; C.-W. Tao; Y.-H. Li
In this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the RGA. Moreover, we propose an application of the RGA in designing an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear dynamical systems. The free parameters of the indirect adaptive fuzzy-neural controller can successfully be tuned on-line via the RGA approach. A supervisory controller is incorporated into the RIAFC to stabilize the closed-loop nonlinear system. An example of a nonlinear system controlled by RIAFC are demonstrated to show the effectiveness of the proposed method.
Gene boundary adjustment of reduced-form genetic algorithms for B-spline neural network training
(2004-01-01) W.-Y. Wang; C.-W. Tao; C.-Y. Kuo; C.-Y. Chu
Identification of Four Types of High-Order Discrete-Time Nonlinear Systems Using Hopfield Neural Networks
(Watam Press, 2007-01-01) W.-Y. Wang; I-H. Li; S.-F. Su; C.-W. Tao
A Novel Fuzzy Ant Colony System for Parameter Determination of Fuzzy Controllers
(中華民國模糊學會, 2009-12-01) C.-W. Tao; J.-S. Taur; J.-T. Jeng; W.-Y. Wang
In this paper, a novel fuzzy ant colony system (FACS) with a fuzzy mechanism and a fuzzy probable mechanism is presented for parameter determinations. Based on the fuzzy rules, the transition behavior of ants is simulated. The fuzzy probable mechanism is introduced with fuzzy probable rules to implement the diverse searching. The fuzzy probable rules are proposed to have the fuzziness in the antecedent parts and the probability in the consequent parts. To indicate the effectiveness, the fuzzy ant colony system is applied to find the proper parameters of the fuzzy sliding controllers for swinging and balancing the inverted pendulum and cart system. Also, the comparisons between the proposed fuzzy ant colony system and other ant colony optimization algorithms are provided in the simulations.
Observer-Based Direct Adaptive Fuzzy-Neural Control for Anti-lock Braking Systems
(中華民國模糊學會, 2006-12-01) G.-M. Chen; W.-Y. Wang; T.-T. Lee; C.-W. Tao
In this paper, an observer-based direct adaptive fuzzy-neural controller (ODAFNC) for an anti-lock braking system (ABS) is developed under the constraint that only the system output, i.e., the wheel slip ratio, is measurable. The main control strategy is to force the wheel slip ratio to well track the optimal value, which may vary with the environment. The observer-based output feedback control law and update law for on-line tuning of the weighting factors of the direct adaptive fuzzy-neural controller are derived. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed control scheme forABS control.
Robust control of the mismatched systems with the fuzzy integral sliding controller
(2003-10-08) C.-W. Tao; M.-L. Chan; W.-Y. Wang
An adaptive fuzzy integral sliding mode controller for mismatched time-varying linear systems is presented in this paper. The proposed fuzzy integral sliding mode controller is designed to have zero steady state system error under step inputs and alleviate the undesired chattering around the sliding surface. The parameters in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy integral sliding mode control system. Thus, the bounds of the uncertainties are not required to be known in advance. The designed fuzzy integral sliding mode control system is shown to be invariant on the sliding surface. Moreover, the reaching mode of the sliding surface is guaranteed and the close-loop system is stable. Simulation results are included to illustrate the effectiveness of the presented fuzzy integral sliding mode controller.
Sliding Control for Linear Uncertain Systems
(2003-09-19) C.-W. Tao; M.-L. Chan; W.-Y. Wang
A new design approach to enhance a terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. The nonlinear sliding surface is used to have the system states arrive at the equilibrium point in the finite time period. The sliding coefficient matching condition is extended for the terminal sliding mode control. The uncertain system with the proposed terminal sliding mode controller is shown to be invariant on the sliding surface. The reaching mode of the sliding surface is guaranteed and the close-loop system is stable. Moreover, the undesired chattering is alleviated with the designed terminal sliding mode controller. Simulation results are included to illustrate the effectiveness of the presented terminal sliding mode controller.
Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control
(2003-10-08) W.-Y. Wang; G.-M. Chen; C.-W. Tao
In this paper, an output feedback direct adaptive fuzzy neural controller for an anti-lock braking system (ABS) is developed. It is assumed that only the system output and the wheel slip ratio, is available for measurement. The main control strategy is to force the wheel slip ratio tracking variant optimal slip ratios, which may vary with the environment and assumed to be known during the vehicle-stopping period. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated.