Accelerated Adaptive Backstepping Control of the Chaotic PMSM via the Type-2 Sequential Fuzzy Neural Network

An accelerated adaptive backstepping controller based on the type-2 sequential fuzzy neural work (T2SFNN) is developed to elimate chaos and ensure the PMSM to maintain highperformance. Firstly, the dynamic model of the PMSM is established to facilitate the design of controller. Then, the phase portrait and Lyapunov exponent are given to illustrate the motion state of system. To suppress nonlinear motion caused by chaos, an accelerated adaptive backstepping controlled is developed wherein the T2SFNN is introduced to realize the approximation of unknown term of the PMSM. The second-order tracking differential is employed to solve the “complex item explosion” in the recursive process of traditional backstepping. In addition, the Lyapunov energy function is used to prevent output constraints from being violated. Finally, numerical simulation experiments show that the designed controller can reach the effects of chaos suppression and accelerated convergence.