TSK Fuzzy CMAC-Based Robust Adaptive Backstepping Control for Uncertain Nonlinear Systems

Lin, Chih‐Min; Li, Hsin-Yi

doi:10.1109/tfuzz.2012.2191789

Cited by 62 publications

(25 citation statements)

References 23 publications

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“…In the chaos synchronization problem, it is required that the slave system can track the trajectories of the master system. Consider two coupled chaotic systems as [39] Master system: (32) where x and y are the system states and u is the control input. In this example, it is assumed that 33 = …”

Section: Simulation Resultsmentioning

confidence: 99%

Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

Hsu

Chang

2016

Neurocomputing

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Section: Simulation Resultsmentioning

confidence: 99%

Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

Hsu

Chang

2016

Neurocomputing

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“…In the presence of the lumped error term ε, consider the following inequality associated with L 2 tracking performance [32][33][34] (22) where η α , η c , η σ , η h , and η ω are positive constants and δ is a prescribed attenuation constant. If the system starts with the initial condition s(0) = 0,α(0) = 0,c(0) = 0,σ(0) = 0, h(0) = 0 andω(0) = 0, the L 2 -gain inequality can be written as…”

Section: Itsmc Systemmentioning

confidence: 99%

“…The attenuation constant δ can be specified by the designer to achieve a desired attenuation ratio of s L2 to ε L2 [32][33][34]. The L 2 -gain property can be used to cope with the control problems owing to system uncertainties.…”

Section: Itsmc Systemmentioning

confidence: 99%

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Intelligent total sliding‐mode control with dead‐zone parameter modification for a DC motor driver

Hsu

2014

IET Control Theory & Applications

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A functional-linked cerebellar model neural network (FCMNN) equipped with sine-cosine perturbed Gaussian basis functions to online approximate an unknown nonlinear term in the system dynamics of a DC motor driver is proposed in this study. The sine-cosine perturbation in the Gaussian basis functions possessing the ability of handling rule uncertainties is quite useful for real-time applications. Then, an intelligent total sliding-mode control (ITSMC) system that is composed of a computation controller and a robust compensator is proposed. The computation controller including an FCMNN approximator is the main controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the FCMNN approximator upon system stability. The online parameter adaptation laws are derived based on a Lyapunov function so that the L 2 tracking performance can be guaranteed. To reduce the parameter overtraining problem, a dead-zone parameter modification scheme is adopted so that the parameter tuning process will be stopped when a tracking index is smaller than a pre-specified threshold. Finally, the proposed ITSMC system is implemented on a 32-bit microcontroller for possible low-cost and high-performance industrial applications. The experimental results show that the ITSMC system can achieve favourable tracking performance and is robust against parameter variations in the plant.

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“…Further, several neural-network-based intelligent backstepping controllers have been developed [9]- [11]. The self-learning ability of neural networks (NNs) is used without requiring preliminary offline tuning.…”

Section: Introductionmentioning

confidence: 99%

Microcontroller-Based B-Spline Neural Position Control for Voice Coil Motors

Hsu

Chen

2015

IEEE Trans. Ind. Electron.

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Since the system dynamic of a voice coil motor (VCM) is difficult to obtain, this paper uses a B-spline neural network (BNN) to online approximate an unknown nonlinear term in the system dynamics of a VCM by tuning its interior parameters. Meanwhile, a B-spline neural position control (BNPC) system which is composed of a position controller and a fuzzy compensator is proposed. A proportional-integral adaptation law is derived to speed up the convergence of tracking error in the sense of projection algorithm and Lyapunov stability theorem. Finally, the proposed BNPC system is implemented on a 32-bit microcontroller. The experimental results show that the proposed BNPC system is robust against payload variations with high accuracy motion response.Index Terms-B-spline neural network, Lyapunov function, microcontroller, voice coil motor.

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TSK Fuzzy CMAC-Based Robust Adaptive Backstepping Control for Uncertain Nonlinear Systems

Cited by 62 publications

References 23 publications

Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

Intelligent total sliding‐mode control with dead‐zone parameter modification for a DC motor driver

Microcontroller-Based B-Spline Neural Position Control for Voice Coil Motors

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