Multilayer feedforward networks are universal approximators
Neural Networks
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Design of fuzzy control systems with guaranteed stability
Fuzzy Sets and Systems
Automatica (Journal of IFAC)
Optimization by Vector Space Methods
Optimization by Vector Space Methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Switching control of an R/C hovercraft: stabilization and smoothswitching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
T-S model based indirect adaptive fuzzy control using online parameter estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
H∞ output tracking control for nonlinear systems via T-S fuzzy model approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability Analysis of Takagi–Sugeno Fuzzy Cellular Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Output tracking control of MIMO fuzzy nonlinear systems using variable structure control approach
IEEE Transactions on Fuzzy Systems
Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
IEEE Transactions on Neural Networks
A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems
Fuzzy Sets and Systems
Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
This paper describes a novel design of an on-line Takagi-Sugeno (T-S) fuzzy-neural controller for a class of general multiple input multiple output (MIMO) systems with unknown nonlinear functions and external disturbances. Instead of modeling the unknown systems directly, the T-S fuzzy-neural model approximates a virtual linearized system (VLS) of a real system with modeling errors and external disturbances. Compared with previous approaches, the main contribution of this paper is an investigation of more general MIMO unknown systems using on-line adaptive T-S fuzzy-neural controllers. In this paper, we also use projection update laws, which generalize the projection algorithm, to tune the adjustable parameters. This prevents parameter drift and ensures that the parameter matrix is bounded away from singularity. We prove that the closed-loop system controlled by the proposed controller is robust stable and the effect of all the modeling errors and external disturbances on the tracking error can be attenuated. Finally, two examples covering four cases are simulated in order to confirm the effectiveness and applicability of the proposed approach in this paper.