Intelligent Maximum Torque per Ampere Tracking Control of Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network

In order to construct a high-performance synchronous reluctance motor (SynRM) drive system, an intelligent maximum torque per ampere (MTPA) tracking control using a recurrent Legendre fuzzy neural network (RLFNN) is proposed in this study. First, a traditional MTPA (TMTPA) control system based on FOC is introduced. Since the reluctance torque of the SynRM is highly nonlinear and time-varying, the MTPA tracking control is very difficult to achieve by using the TMTPA control in practical applications. Then, an adaptive computed current (ACC) speed control using the proposed RLFNN for the MTPA tracking control of a SynRM drive system, which does not use a lookup table and can effectively obtain the optimal current angle command of MTPA online, is described in detail. The ACC speed control is applied to generate the stator current magnitude command, and an adaptation law is proposed to online adapt the value of a lumped uncertainty in the ACC control. Moreover, the adaptation law is derived using the Lyapunov stability theorem to guarantee the asymptotic stability of the ACC speed control. Furthermore, the proposed RLFNN is employed to produce the incremental command of the current angle. In addition, the ACC speed control and RLFNN are implemented in a TMS320F28075 32-bit floating-point digital signal processor for a 4 kW SynRM drive system. Finally, the robustness and effectiveness of the proposed intelligent MTPA tracking control are verified by some experimental results.