The purpose of this project is to develop a digital signal processor (DSP)-based high-performance synchronous reluctance motor (SynRM) drive system. In order to increase the control performance of the SynRM, the intelligent control systems are developed in this project. In the first year of this project, the simulation system of the high-performance SynRM is designed, and the dynamic model of high-performance SynRM system is also analyzed and derived. Moreover, the DSP-based motor drive and control system is developed to actuate the high-performance SynRM drive system. The SynRM drive system is highly nonlinear and very sensitive to parameter variations and external disturbance, especially the torque of the SynRM is nonlinear and time-varying and very sensitive to the variations of the inductance and current. Therefore, to enhance the efficiency and performance of the SynRM drive system is the most important issue in the whole development of the system. Thus, in the second year of this project, an intelligent backstepping control (BSC) system is proposed for the position control of the high-performance SynRM drive system. Comparing with the traditional BSC, the recurrent feature selection fuzzy neural network (RFSFNN) is the main controller and used to approximate the idea BSC with online learning and fast convergence capabilities in order to alleviate the chattering phenomena owing to the sign function. In addition, to compensate the possible approximated error of the RFSFNN, an adaptive compensator is designed and augmented. As a result, the proposed intelligent BSC system will be able to have a satisfactory tracking performance with guaranteed stability. Furthermore, an adaptive backstepping control (ABSC) based Lagrange multiplier (LM) maximum torque per ampere (MTPA) control will be proposed and developed to improve the control performance of the SynRM drive system in the third year of this project. Owing to the saturation effect, the optimal performance of the traditional MTPA control is very difficult to obtain in practical applications. Thus, an ABSC based LM MTPA control using the LM to obtain the current command of the direct and quadrature axis is proposed to achieve the optimal MTPA of the high-performance SynRM drive system. Then, the adaptive law can estimate the required inductance to alleviate the saturation effect. Additionally, an adaptive law is developed to improve the control performance and to achieve the requirement of stability for the high-performance SynRM drive system. The adaptive law is derived using the Lyapunov stability theorem to update the control parameters in real-time. A TMS320F28075 DSP made by Texas Instruments is the core of the proposed control system. Moreover, the proposed control algorithms are realized in the DSP using the “C” language. Furthermore, a DSP extension board reads the rotor position, motor speed and phase currents from the sensors using the encoder interface circuit and analog to digital converters. Therefore, the DSP-based of the high-performance SynRM drive system can be achieved.