Nonlinear control of benchmark problems using TSK-type fuzzy neural network

Lee, Ching Hung; Lai, Wei

doi:10.1007/s00521-012-1250-5

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Cited by 3 publications

(1 citation statement)

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“…Though the fuzzy neural network (FNN) possesses good reasoning and learning capabilities according to the changeable fuzzy membership functions and connective weights [20, 21], the fuzzy operators such as MIN, MAX, product, and algebraic sum are always static. Intuitively, the FNN with unchangeable fuzzy operators is not adaptive and optimal with respect to the non‐linear dynamic systems [22].…”

Section: Introductionmentioning

confidence: 99%

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Chen

Lee

2017

IET Control Theory & Applications

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Section: Introductionmentioning

confidence: 99%

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Chen

Lee

2017

IET Control Theory & Applications

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Compensatory fuzzy neural network control with dynamic parameters estimation for linear voice coil actuator

Chen

Lee

2017

Precision Engineering

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Fault Detection for T-S Fuzzy Time-Delay Systems: Delta Operator and Input-Output Methods

Gao

et al. 2015

IEEE Trans. Cybern.

225

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This paper focuses on the problem of fault detection for Takagi-Sugeno fuzzy systems with time-varying delays via delta operator approach. By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem. The time-varying delay is approximated by the two-term approximation method. Fuzzy augmented fault detection system is constructed in δ -domain, and a threshold function is given. By applying the scaled small gain theorem and choosing a Lyapunov-Krasovskii functional in δ -domain, a sufficient condition of asymptotic stability with a prescribed H∞ disturbance attenuation level is derived for the proposed fault detection system. Then, a solvability condition for the designed fault detection filter is established, with which the desired filter can be obtained by solving a convex optimization problem. Finally, an example is given to demonstrate the feasibility and effectiveness of the proposed method.

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Nonlinear control of benchmark problems using TSK-type fuzzy neural network

Cited by 3 publications

References 20 publications

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Compensatory fuzzy neural network control with dynamic parameters estimation for linear voice coil actuator

Fault Detection for T-S Fuzzy Time-Delay Systems: Delta Operator and Input-Output Methods

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