Novel adaptive fuzzy neural network controller for a class of uncertain non-linear systems

Abstract

In this paper, an adaptive fuzzy neural network (AFNN) controller with a state observer approach based on novel adaptive particle swarm optimization-simulated annealing (NAPSO-SA) for a class of non-linear systems is proposed. First, NAPSO-SA is used to adjust the parameters of the FNN, while adaptive laws are used to approximate unknown non-linear functions and the unknown upper bounds of uncertainties respectively. Second, a state observer is applied for estimating all states that are not available for measurement in the system. By using the strictly-positive-real (SPR) stability theorem, the proposed controller not only guarantees the stability of a class of non-linear systems but also maintains good tracking performance. The novel intelligence algorithm generates optimal parameters for the control schemes and is developed to guarantee the asymptotically stability of the system. Finally, simulation results substantiate the fact that the proposed method stands out as offering better properties than the observer-based adaptive fuzzy control (OBAFC) for tracking performance.