An adaptive neuro-fuzzy with nonlinear PID controller design for electric vehicles

Abstract

In this work, an adaptive neuro-fuzzy (ANF) with a nonlinear proportional integral derivative (NLPID) controller (ANF-NLPID) has been proposed to solve the speed-tracking problem in electric vehicles (EVs) with brushless DC motor (BLDC). The ANF-NLPID controller eliminates the external disturbances caused by environmental or internal issues and uncertainties caused by parameter variations that lead to insufficient speed-tracking performance and increased energy consumption in EVs. An improved particle swarm optimization based on the chaos theory (IPSO-CT) algorithm is introduced to obtain the parameters of the fuzzy logic controller membership function and nonlinear PID controller and present the optimal performance for the EV. Employing the chaos technique with PSO helps to prevent the system from being trapped in the local minimum or optimum problem. The performance of the IPSO-CT algorithm is tested using a numerical comparison with other existing works. The outstanding performance of the ANF-NLPID controller has been evaluated by measuring the speed-tracking performance for the new European driving cycle (NEDC) and circular trajectories. Three case studies have been presented based on measuring the ANF-NLPID controller performance without disturbances, with disturbances, with disturbances, and uncertainties effects, respectively. Furthermore, the ANF-NLPID controller has been employed in different EV models to study the performance of this type of controller. Each of the three cases includes other existing works along with the ANF-NLPID controller to provide an insightful comparison using statistical functions to obtain each controller’s overall objective function value. The other existing works are fuzzy fractional order PID (Fuzzy FOPID), fuzzy integer order PID (Fuzzy IOPID), and integer order PID (IOPID) controllers. A sensitivity analysis has been conducted to test the proposed controller’s ability to present high speed-tracking performance while changing the disturbances and uncertainty rates. The results demonstrate that the ANF-NLPID controller is superior in speed-tracking control regulation for the new European cycle drive (NEDC) and circular speed trajectories and overcomes the external disturbances and uncertainties problem with low error results. In the end, the results reveal that the ANF-NLPID controller is more efficient than the fuzzy FOPID, fuzzy IOPID, and IOPID controllers in each case.