Model-predictive control based on Takagi-Sugeno fuzzy model for electrical vehicles delayed model

• Published in: IET electric power applications 2017-05, Vol.11 (5), p.918-934
• Main Authors: Khooban, Mohammad Hassan, Vafamand, Navid, Niknam, Taher, Dragicevic, Tomislav, Blaabjerg, Frede
• Format: Article
• Language: English
• Subjects: delay systems; direct current motor; electric vehicles; electrical vehicles delayed model; federal test procedure; fuzzy control; linear matrix inequalities; Lyapunov methods; model‐predictive control; nonquadratic Lyapunov function; parameter uncertainty; predictive control; robust control; Special Issue: Advances in Predictive Control of Variable-Speed Electric Drives; speed control; Takagi‐Sugeno fuzzy model; time‐delay states; time‐varying parameters; time‐varying systems; TMS320F28335 DSP; velocity control
• ISSN: 1751-8660; 1751-8679; 1751-8679
• DOI: 10.1049/iet-epa.2016.0508

Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation and experimental results demonstrate the flawless performance of the suggested controller and the fast and accurate tracking of the EV speed to its set-point.