Fuzzy Fractional-Order PID Based on A COVID-19 Optimization Tracking Control for Electric Vehicle

This paper seeks to enrich the dynamic performance of Electric Vehicle (EV). The key to achieve this purpose is selecting advanced control strategies. Untraditional COVID-19 optimization technique had been utilized to determine the correct parameters values for the fuzzy FOPID, fuzzy PID, and PID controllers. These controllers are executed on the electric vehicle (EV) to track a preselected speed profile. The foremost objective of the developed controllers is to improve the behavior of the EV such as shrinking the steady-state error, decreasing the rise time, and increasing comfortability. Most of the EV models were high-order systems and have uncertainty parameters. So, this paper uses fuzzy logic control and COVID-19 optimization to overcome these problems. Each control technique is optimized using the COVID-19 algorithm based on several new objective functions. Three cases were considered to investigate the proposed controllers, the first case was applied on a stationary reference speed while the second case was executed at vehicle acceleration and the last case was implemented when the vehicle deceleration. The presented results display that the fuzzy FOPID controller adjusted by the COVID-19 optimization can absorb the EV uncertainty and nonlinearity. It achieved a minimum rise-time and the least steady-state-error compared to the other control techniques. Moreover, it has no overshoot in the speed which guarantees comfort for the EV.