Analysis, control and design of speed control of electric vehicles delayed model: multi‐objective fuzzy fractional‐order controller

The purpose of this study is to suggest an optimal multi‐objective fuzzy fractional‐order controller (MOFFOPID) for the speed control of EV systems with time‐delay. It is presumed that while the EV is in movement, the armature winding resistance of the direct current (DC) motor varies with time due to temperature changes and this makes it impossible to develop an accurate dynamic model for an EV system. Consequently, in order to have a fast and accurate tracking of the set‐point, besides a smooth control signal, a new multi‐objective stochastic optimisation is used for the online adjustment of the parameters of MOFFOPID controller. Moreover, a comparison is made between the results of the current study and those of some of the most recent studies on the same topic, which have used online multi‐objective PI and online multi‐objective fuzzy PI, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are implemented on a DC motor to verify the effectiveness of the proposed MOFFOPID controller in controlling the speed of the DC motor which has non‐linear features. The results of the simulation confirm the desirable performance of suggested controller.