Adaptive Tracking Control of Single Input Single Output Nonlinear System with Sectorial Dead Zone Using Interval Type-2 Neural Network Fuzzy Control

Abstract

Single-input, single-output (SISO) nonlinear systems have problems with sectorial dead zone nonlinearities, noise, uncertainties, approximation errors, and external disturbances. Therefore, we developed an interval Type-2 neural network fuzzy adaptive controller (IT2-NNFAC) for satisfactory H-infinity (H ∞ ) tracking performance to solve the problems of the SISO system. To adjust the parameters of the proposed IT2-NNFAC, a structure of the fuzzy logic inference system and online adaptive laws are adopted, which are based on the Lyapunov stability criterion and Riccati inequality. All systems with the proposed IT2-NNFAC attenuate the effect of external disturbances on tracking errors at any specified level. In the proposed IT2-NNFAC, all the signals in the closed-loop system guarantee uniform and ultimate boundedness and satisfactory tracking performance with the proper Lyapunov stability criterion and Riccati inequality. H ∞ tracking responses and the resilience and efficacy of the proposed IT2-NNFAC were proved by testing a mass spring damper system with sectorial dead zone nonlinearities, uncertainties, and external disturbances.