Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO

This work presents a design of interval type-2 fuzzy brain emotional learning control (T2FBELC) combining with the self-evolving algorithm to help the network to automatically achieve the optimum construction from the empty initial rule. In the control system design, the T2FBELC is the main controller used to mimic an ideal controller, and a robust controller is a compensator for the compensation of the residual error. Implementing the steepest descent gradient approach, the parameter adaptive laws of the proposed system are derived. Besides, the particle swarm optimization (PSO) is applied to find the optimal learning rates for the parameter adaptive laws. The stability of the proposed algorithm is guaranteed using the Lyapunov function. Finally, the effectiveness of the proposed control system is verified by numerical simulations of the chaotic systems.