Brief paper: An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems

Authors:
S. P. Moustakidis;G. A. Rovithakis;J. B. Theocharis
Affiliations:
Aristotle University of Thessaloniki, Department of Electrical and Computer Engineering, 54124, Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Electrical and Computer Engineering, 54124, Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Electrical and Computer Engineering, 54124, Thessaloniki, Greece
Venue:
Automatica (Journal of IFAC)
Year:
2008

Citing 8
Cited 2

A universal construction of Artstein's theorem on nonlinear stabilization

Systems & Control Letters
Stable adaptive control using fuzzy systems and neural networks

IEEE Transactions on Fuzzy Systems
Stable multi-input multi-output adaptive fuzzy/neural control

IEEE Transactions on Fuzzy Systems
Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model

IEEE Transactions on Fuzzy Systems
Stable model reference adaptive fuzzy control of a class of nonlinear systems

IEEE Transactions on Fuzzy Systems
Stable adaptive neuro-control design via Lyapunov function derivative estimation

Automatica (Journal of IFAC)
Robust redesign of a neural network controller in the presence of unmodeled dynamics

IEEE Transactions on Neural Networks
Neural net robot controller with guaranteed tracking performance

IEEE Transactions on Neural Networks

Control of unknown nonlinear systems with efficient transient performance using concurrent exploitation and exploration

IEEE Transactions on Neural Networks
Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	22.15

Visualization

Abstract

An adaptive neuro-fuzzy control design is suggested in this paper, for tracking of nonlinear affine in the control dynamic systems with unknown nonlinearities. The plant is described by a Takagi-Sugeno (T-S) fuzzy model, where the local submodels are realized through nonlinear dynamical input-output mappings. Our approach relies upon the effective approximation of certain terms that involve the derivative of the Lyapunov function and the unknown system nonlinearities. The above task is achieved locally, using linear in the weights neural networks. A novel resetting scheme is proposed that assures validity of the control input. Stability analysis provides the control law and the adaptation rules for the network weights, assuring uniform ultimate boundedness of the tracking and the signals appearing in the closed-loop configuration. Illustrative simulations highlight the approach.