Electronically Tunable ACO Based Fuzzy FOPID Controller for Effective Speed Control of Electric Vehicle

Abstract

The phenomenal growth of the Electric Vehicle (EV) technology demands efficient and intelligent control strategies for the propulsion system. In this work, a novel fuzzy fractional order PID (FOPID) controller using Ant Colony Optimization (ACO) algorithm has been proposed to control EV speed effectively. The controller parameters and the fuzzy logic controller’s membership functions are tuned and updated in real-time using the multi-objective ACO technique. The proposed controller’s speed tracking performance is verified using the new European driving cycle (NEDC) test in the MATLAB-Simulink platform. The proposed controller outperforms the ACO-based fuzzy integer-order PID (IOPID), FOPID, and traditional IOPID controllers. The sensitivity analysis confirms the robustness of the proposed controller for varying parameters of the EV model. The stabilization of EV speed in the presence of external disturbance is also confirmed. In the proposed work, an attempt is made to analyze the system’s stability using Matignon’s theorem, considering the linearized EV model. The proposed controller gives optimum speed tracking performance compared to the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) based fuzzy FOPID controllers. Additionally, the optimized fuzzy FOPID controller is realized using a second-generation current conveyor with extra inputs (EX-CCII) and fractional-order capacitors with electronic tunability. The controller circuit’s performance evaluation is carried out in the Cadence Analog Design Environment using GPDK 180 nm CMOS process.

Keywords

• Ant colony optimization
• electric vehicle
• multi-objective optimization
• fuzzy FOPID
• second-generation current conveyor with extra inputs