Open AccessModel‐predictive control based on Takagi‐Sugeno fuzzy model for electrical vehicles delayed model
Author(s) -
Khooban Mohammad Hassan,
Vafamand Navid,
Niknam Taher,
Dragicevic Tomislav,
Blaabjerg Frede
Publication year - 2017
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2016.0508
Subject(s) - control theory (sociology) , robustness (evolution) , model predictive control , fuzzy logic , computer science , lyapunov function , fuzzy control system , controller (irrigation) , robust control , control engineering , engineering , control system , control (management) , nonlinear system , artificial intelligence , biochemistry , chemistry , physics , electrical engineering , quantum mechanics , biology , agronomy , gene
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.