Acta Polytechnica Hungarica Vol. 19, No. 5, 2022 – 85 – Adaptive Fractional Order Sliding Mode Speed and Current Control for Switched Reluctance Motor Ashraf Abdalla Hagras Egyptian Atomic Energy Authority (EAEA), Abo Zaabal, 13759 Cairo, Egypt; ashraf.hagras@eaea.org.eg Abstract: This paper applies a Fractional Order Sliding Mode Control (FOSMC) to the two loops speed (the outer loop) and current (the inner loop), for Switched Reluctance Motor (SRM). This approach proposes a new, simple and fast switching control law for the Fractional Order Sliding Mode Control (FOSMC), characterized by its simplicity of design, flexibility of control and adaptive capability. The proposed controller is based on nonsingular terminal SM surface. The stability of the proposed approach was analyzed and guaranteed, using the Lypunov stability theory. This new scheme achieved minimum torque and speed ripples. Simulation results using MATLAB/SIMULINK validated the improved performance of the proposed approach against parameters variations, external disturbances and measurement noise, by comparing it with PI, Neural Network Controller (NNC), Hysteresis Controller (HC) and conventional Sliding Mode Controllers (SMC). Keywords: Fractional order SMC; Speed Control; Current Control; Switched Reluctance Motor (SRM); Neural Network Controller (NNC) 1 Introduction Switched Reluctance Motor (SRM) is a low-cost machine because it has neither winding nor permanent magnets in the rotor nor brushes nor commutators. Also, it is reliable because their phases can be feed independently and connected in series with the DC power supply. This makes its drive robust compared to the other electrical machines drives. Besides, its drive is simple because of its unidirectional current. However, it has high coupling and nonlinearities between the current and the position [10]. Torque ripples and high starting torque are other obstacles. Therefore, PI can’t overcome these difficulties and advanced control methodologies were proposed to overcome these disadvantages [1, 2, 26, 27]. Speed control of the motor was carried using PI but its gains were tuned using bat algorithm [3] or using ant-colony optimization algorithm [4] or using Particle Swarm Optimization (PSO) and the Zeigler Nicholas method [5, 8] to improve the