Design of self-tuning fuzzy sliding mode control for TORA system

Hung, Lon-Chen; Lin, Hung-Ping; Chung, Habong

doi:10.1016/j.eswa.2005.11.008

Cited by 90 publications

(46 citation statements)

References 15 publications

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“…However, the time-invariant sliding surface-based signed distance still suffers from the slow convergence speed. A self-tuning signed-distance fuzzy sliding-mode control (DSSFSMC) and a decoupled sliding-mode control based on neural network methods are proposed to improve the convergence speed Hung, Lin, & Chung, 2007). However, these methods are based on complicated algorithms which increase the complexity of the controller design.…”

Section: Introductionmentioning

confidence: 99%

Decoupled sliding-mode controller based on time-varying sliding surfaces for fourth-order systems

Yorgancıoğlu

Kömürcügil

2010

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Decoupled sliding-mode controller based on time-varying sliding surfaces for fourth-order systems

Yorgancıoğlu

Kömürcügil

2010

Expert Systems with Applications

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“…The respective simulation results are shown in Figs. 6-9. Fuzzy logic in cooperation with sliding mode control is being used in many papers, for example [26][27][28][29][30][31], to replace the discontinuous signum function at the reaching phase in traditional sliding-mode control.…”

Section: Sliding Surfaces Through Fractional Pd μ Controllermentioning

confidence: 99%

Fractional order control of a coupled tank

et al. 2010

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In this paper, a hybrid system that combines the advantages in terms of robustness of the fractional control and the Sliding Mode Control (SMC) will be proposed. The proposed fractional order SMC is applied to a level control in a nonlinear coupled tank, as a case study. To investigate the capability of the method, a Sliding Mode Controller is alternatively designed. Primarily a sliding surface based on linear compensation networks PD or PID is designed. The work is followed by designation of a fractional form of these networks, PD μ or PI λ D μ . Finally, the performance of the proposed technique is also investigated under disturbance and variation in parameters of system. The simulation results indicate the significance of the fractional order sliding mode controllers.

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“…By incorporating with sliding-mode control, Lin and Hsu have proposed the adaptive fuzzy sliding-mode control system (Lin & Hsu, 2002, 2003a. Based on the same idea, Hung and Chung presented the self-tuning fuzzy sliding mode control and adaptive neural network-based sliding-mode control for nonlinear systems (Hung, Lin, & Chung, 2007a, 2007b. Moreover, a number of investigators have proposed the adaptive neural network control techniques for SISO nonlinear plants with unknown nonlinear function (Leu, Lee, & Wang, 1997;Wang, Lin, Lee, & Liu, 2002;Wang, Chan, Hsu, & Lee, 2002).…”

Section: Introductionmentioning

confidence: 95%