Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
CMAC with general basis functions
Neural Networks
Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive CMAC-based supervisory control for uncertain nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Applied Soft Computing
Robust PID TS fuzzy control methodology based on gain and phase margins specifications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, a robust intelligent tracking control (RITC) system employs an adaptive output recurrent cerebellar model articulation controller (ORCMAC) is developed for uncertain nonlinear system to achieve H^~ tracking performance. The proposed dynamic structure of ORCMAC has superior capability in efficient learning mechanism and dynamic response. Temporal relations are embedded in conventional cerebellar model articulation controller (CMAC) by adding feedback connections between the output space and input space, so that the ORCMAC captures the system dynamic. In the RITC design, the Taylor linearization technique is employed to increase the learning ability of ORCMAC and the on-line adaptive laws are derived based on the Lyapunov stability analysis, the sliding mode control methodology and the H^~ control technique so that the stability of the closed-loop system and H^~ tracking performance can be guaranteed. Finally, the proposed control system is applied to control an inverted pendulum system, a Van der Pol oscillator and a Genesio chaotic system. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performances for the uncertain nonlinear systems with unknown dynamic functions and under the occurrence of external disturbance.