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Reliability is of great concern in applications like automotive, aerospace and home appliances in which Switched Reluctance Motor (SRM) is getting more popularity. Therefore, the reliability of the motor drive hardware and control algorithm should be ensured. In this study, the reliability and complexity of several widely used control methods for SRM drive are described using theoretical and simulation analysis. The effect of common errors associated with a practical SRM drive on its control systems' reliability is analyzed. In addition, an investigation on the relationship between system complexity and reliability of SRM control based on information flow complexity within the control technique is also presented. Three methods including Current Chopping Control (CCC), Torque Sharing Function based Direct Instantaneous Torque Control (TSF-DITC) and Direct Torque Control (DTC) are considered and compared. The results obtained showed that the CCC method shows the highest robustness to measurement error followed by the TSF-DITC then the DTC. However, the TSF-DITC method achieved superior torque characteristics while maintaining high robustness and reliability.

Several studies have proved that sensorless control methods can improve the overall robustness and costs of a drive while maintaining the desired performance. However, the general approach of these sensorless strategies involves flux estimation which is significantly affected by the uncertainties associated with the machine's terminal measurement errors and noise. In this paper, a simple robust sensorless control of Switched Reluctance Machine (SRM) is described. The inherent robustness to parameter variations and measurement noise coupled with the high stability and simple computation of a Sliding Mode Observer (SMO) is utilized to eliminate the errors often involved in a fluxlinkage based position estimation for the SRM. The proposed method is tested under different operating conditions. Results obtained show that it is reliable and less susceptible to errors and noise commonly found in sensorless control of SRM. The method was also found to handle the model uncertainties associated with the approximated model used for the estimation but with reduced performance at low speed.I.

This paper presents the application of model predictive controller for controlling a nonlinear 2D gantry crane system with a DC motor as an actuator. The gantry crane system (GCS) dynamics is derived using Lagrange equation method. A model predictive controller is designed based on the linearised GCS and prediction cost function to ensure accurate positioning and oscillation reduction. Simulation via MATLAB and Simulink was performed to investigate the performance of the model predictive controller on the GCS. The controller test was done under several elements altering the behaviour of the system. The closed loop system was analysed considering different cable length, payload mass and trolley position. It was found that the closed loop control meets the main goal of this work, trolley positioning as fast as possible with minimum payload swinging all within a robust input voltage.