Design of ANN-Optimized Second Order Sliding Mode Control of Switched Reluctance Motor for Aircraft Actuators

In this article, a speed control strategy for switched reluctance motors used in aircraft actuators is proposed. SRMs are desirable for aerospace applications due to their high torque-to-inertia ratio, compact size, fault tolerance, and robustness. The proposed control strategy utilizes a cascade control system of an artificial neural network–optimized second-order sliding mode control to handle nonlinearity and parameter uncertainty in SRMs. The SOSMC parameters are tuned using particle swarm optimization. Simulation results using MATLAB/Simulink software demonstrate the effectiveness of the proposed controller compared to sliding mode control and SOSMC controllers. The proposed controller shows improved transient response with advancements in rise time and settling time, reduced torque ripple, and outperforms classical SMC and SOSMC controllers. These findings suggest that the proposed control strategy has the potential to enhance the performance of the system for aircraft actuator applications, aligning with the more electric aircraft strategy by reducing weight, complexity, operational costs, and environmental impacts. The effectiveness of the proposed controller is verified using the simulation results.