Browsing by Author "Lin, Chih-Min"

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Adaptive backstepping tracking control using self-organizing fuzzy neural network
(2006-01-01)
Lin, Chih-Min
;
Hsu, Chun-Fei
;
Chung, I-Fang
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This papers proposes an adaptive backstepping tracking control (ABTC) via self-organizing fuzzy neural network (SOFNN) approach. The proposed ABTC system is comprised of a backstepping tracking controller and an L, controller. The backstepping tracking controller containing a SOFNN identifier is the principal controller, and the L, controller is designed to achieve a tracking performance with desired attenuation level. The SOFNN identifier is used to online estimate the system dynamics with structure and parameter learning. Finally, the proposed ABTC is applied to control a chaotic dynamic system. The simulation results verify that the proposed ABTC system can achieve favorable tracking performance by incorporating of neural network approach and adaptive backstepping control technique.
Adaptive neuro-wavelet control for switching power supplies
(2007-01-01)
Lin, Chih-Min
;
Hung, Kun-Neng
;
Hsu, Chun-Fei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The switching power supplies can convert one level of electrical voltage into another level by switching action. They are very popular because of their high efficiency and small size. This paper proposes an adaptive neuro-wavelet (ANW) control system for the switching power supplies. In the ANW control system, a neural controller is the main controller used to mimic an ideal controller and a compensated controller is designed to recover the residual of the approximation error. In this study, an online adaptive law with a variable optimal learning-rate is derived based on the Lyapunov stability theorem, so that not only the stability of the system can be guaranteed but also the convergence of controller parameters can be speeded up. Then, the proposed ANW control system is applied to control a forward switching power supply. Experimental results show that the proposed ANW controller can achieve favorable regulation performance for the switching power supply even under input voltage and load resistance variations.
Intelligent backstepping control for chaotic systems using self-growing fuzzy neural network
(2006)
Lin, Chih-Min
;
Hsu, Chun-Fei
;
Chung, I-Fang
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes an intelligent backstepping control (IBC) for the chaotic systems. The IBC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-growing fuzzy neural network (SGFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SGFNN identifier. Finally, simulation results verify that the IBC system can achieve favorable tracking performance.
Robust intelligent backstepping longitudinal control of vehicle platoons with H-infinity tracking performance
(2006)
Peng, Ya-Fu
;
Hsu, Chun-Fel
;
Lin, Chih-Min
;
Lee, Tsu-Tian
;
電控工程研究所
This paper proposes a robust intelligent backsteppingcontrol (RIBC) scheme for the car-following control of a platoon of automated vehicles using a recurrent cerebellar model articulation controller (RCMAC) via the H-infinity control technique, so that the robust tracking performance can be achieved. In the RIBC system, an adaptive RCMAC is used to mimic an ideal backstepping control law and a robust controller is designed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Moreover, the Taylor linearization technique is employed to derive the linearized model of the RCMAC. The adaptation laws of the RIBC system are derived on the basis of the Lyapunov stability analysis and H-infinity control theory so that the stability of the system can be guaranteed. Finally, the simulation results denominate that the proposed RIBC system can achieve favorable tracking performance for a safe car-following control.
Uncertainty Analysis on Precision Measurement for Polystyrene Nanospheres Using Dynamic Light Scattering
(2010)
Pan, Shan-Peng
;
Weng, Han-Fu
;
Lin, Chih-Min
;
Liu, Tzong-Shi
;
機械工程學系
Dynamic light scattering (DLS) is the most commonly used technique for measuring nanosphere sizes. In order to establish the traceability of the DLS method to SI units, relevant parameters have been measured in this study. Several studies have been reported on error sources in DLS. However, these studies lacked a systematic method of analyzing the uncertainty of DLS. In this paper we describe the DLS method and present a measurement uncertainty budget. Monodispersed polystyrene latex (PSL) spheres are selected as reference materials in the uncertainty evaluation. The measured nanosphere sizes are 20, 50, 100, 300, 500, and 1000 nm, among which the measurement results of 100, 300, and 500 nm nanospheres obtained using DLS are compared with those for an electrogravitational aerosol balance (EAB) method. The uncertainties for both methods are calculated, and the results of repeated measurements are presented with confidence levels of 95%. (C) 2010 The Japan Society of Applied Physics
Wavelet adaptive backstepping control for a class of nonlinear systems
(2006-09-01)
Hsu, Chun-Fei
;
Lin, Chih-Min
;
Lee, Tsu-Tian
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a wavelet adaptive backstepping control (WABC) system for a class of second-order nonlinear systems. The WABC comprises a neural backstepping controller and a robust controller. The neural backstepping controller containing a wavelet neural network (WNN) identifier is the principal controller, and the robust controller is designed to achieve L-2 tracking performance with desired attenuation level. Since the WNN uses wavelet functions, its learning capability is superior to the conventional neural network for system identification. Moreover, the adaptation laws of the control system are derived in the sense of Lyapunov function and Barbalat's lemma, thus the system can be guaranteed to be asymptotically stable. The proposed WABC is applied to two nonlinear systems, a chaotic system and a wing-rock motion system to illustrate its effectiveness. Simulation results verify that the proposed WABC can achieve favorable tracking performance by incorporating of WNN identification, adaptive backstepping control, and L-2 robust control techniques.