Universal approximation using radial-basis-function networks
Neural Computation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Modeling, identification, and control of a class of nonlinear systems
IEEE Transactions on Fuzzy Systems
A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems
IEEE Transactions on Fuzzy Systems
Direct adaptive control of partially known nonlinear systems
IEEE Transactions on Neural Networks
Neuro-sliding mode control with its applications to seesaw systems
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.