A novel adaptive interval type-3 neuro-fuzzy robust controller for nonlinear complex dynamical systems with inherent uncertainties

A novel observer-based control policy based on an interval type-3 fuzzy logic system is developed to tackle the main limitations of fuzzy-based controllers in sense of approximation of uncertainties and analyzing nonlinear complex systems without detailed dynamics model information. For this purpose, a novel scheme is proposed that includes online optimized tuning rules, a simple type reduction method, and adaptive mechanisms. Also, an adaptive compensator is implemented to enhance the robust performance of the closed-loop system and reduce the effect of approximation errors. For the stability analysis, appropriate Lyapunov functions and Barbalat’s lemma are employed. By both simulations and experimentally implementation, it is shown that the suggested approach results in a more accurate approximation of unknown models and complicated nonlinearities, and good resistance against uncertainties and parameter variations.