This work presents modeling, driving and classical speed control techniques for the switched reluctance motor. The aim is to improve the computational model, the control response and the machine efficiency. A parametric regression model was used to find the inductance profile of the switched reluctance motor and from the new inductance profile model. The drive and control techniques are shown: (i) with speed control acting on the excitation voltage and fixed switching angles, (ii) with speed control acting on the switching angles and fixed excitation voltage and (iii) with speed control acting on the excitation voltage, in this case, with dynamic switching angles and controller parameters. The inductance profile is represented by expression and inserted into the machine computer model, allowing greater precision and low computational cost. The speed control acting on the excitation voltage with dynamic controller parameters and dynamic switching angles allowed: (i) shorter response time for a wide range of control, (ii) higher efficiency, (iii) low computational cost and (iv) simplified implementation and maintenance. The techniques proposed in this work obtained precision of the computational model with respect to the system (in workbench) and optimized parameters in a wide range of the speed control, allowing an improvement of switched reluctance motor efficiency.

Additional Metadata
Keywords Classical control, Driving and control, Identification and modeling systems, Improve efficiency, Optimized techniques, Simulation, Switched reluctance motor
Persistent URL dx.doi.org/10.1016/j.conengprac.2019.06.007
Journal Control Engineering Practice
Citation
da Cunha Reis, M.R. (Marcio Rodrigues), de Araujo, W.R.H. (Wanderson Rainer Hilario), Gomes, V.M. (Viviane Margarida), dos Santos e Silva, F. (Felippe), Ganzaroli, C.A. (Cleber Asmar), Gomes, F.A. (Flavio Adalberto), … Calixto, W.P. (Wesley Pacheco). (2019). Optimized techniques for driving and control of the switched reluctance motor to improve efficiency. Control Engineering Practice, 90, 1–18. doi:10.1016/j.conengprac.2019.06.007