Optimal Type-2 Fuzzy Controller for Anti-lock Braking Systems

Anti-lock Braking System (ABS) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. Intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. For this reason an interval type-2 fuzzy controller is developed to improve the performance of ABS in presence of uncertainty such as changing road condition. The output membership functions have been optimized by Discrete Action Reinforcement Learning Automata (DARLA) technique. Simulation results show the effectiveness of the proposed controller in comparison to type-1 fuzzy controller.