Fuzzy model reference adaptive control

Authors:
N. Golea;A. Golea;K. Benmahammed
Affiliations:
Electr. Eng. Inst., Oum-El-Bouaghi Univ.;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2002

Citing 0
Cited 17

Nonlinear model reference adaptive control using Takagi-Sugeno fuzzy systems

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Relaxed conditions in tracking control design for a TS fuzzy model

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Robust wavelet-based adaptive neural controller design with a fuzzy compensator

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Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints

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Neuro based model reference adaptive control of a conical tank level process

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Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms

Expert Systems with Applications: An International Journal
Adaptive fuzzy wavelet neural controller design for chaos synchronization

Expert Systems with Applications: An International Journal
Fuzzy model reference adaptive control system

ICAI'05/MCBC'05/AMTA'05/MCBE'05 Proceedings of the 6th WSEAS international conference on Automation & information, and 6th WSEAS international conference on mathematics and computers in biology and chemistry, and 6th WSEAS international conference on acoustics and music: theory and applications, and 6th WSEAS international conference on Mathematics and computers in business and economics
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance

Engineering Applications of Artificial Intelligence
Adaptive control of discrete-time state-space T--S fuzzy systems with general relative degree

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Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

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Abstract

This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.