Direct adaptive neuro-fuzzy trajectory tracking of uncertain nonlinear systems

This paper proposes a direct adaptive neuro-fuzzy controller to address the adaptive tracking problem for a class of affine nonlinear multi-variable multi-input (MVMI) unknown systems that are linearizable by nonlinear state feedback. The proposed control scheme uses a recurrent neuro-fuzzy model to approximate the system, which combines an underlying fuzzy model with the approximation abilities of high-order neural networks to produce the fuzzy recurrent high-order neural network approximator. A hybrid control scheme that combines an adaptive feedback linearization term and a sliding mode term improves system performance by suppressing the influence of external disturbances and approximation errors. The existence and boundedness of the control signal is always assured by employing the novel method of parameter modified hopping and incorporating it in weight updating laws making the closed-loop system Lyapunov stable. The case of SISO systems is considered separately by following similar analysis with the more general MVMI case. Simulations performed on well-known benchmarks demonstrate the effectiveness of the proposed control scheme. Copyright (c) 2012 John Wiley & Sons, Ltd.