Deep Learning-Based PID Controller Tuning for Effective Speed Control of DC Shunt Motors

Abstract

Electric vehicles (EVs) have become essential due to the depletion of fuel energy resources. DC machines and their role in EVs are gaining significant attention. The speed of DC motor-driven wheels in EVs is usually controlled by proportional-integral-derivative (PID) controllers. But, when the EV is running, the mechanical noises, reduction in tire air volume, the corrugated and rugged surface on which it is driven, etc., lessen the robustness of the PID controllers. This continuous disturbance and variation in speed could result in the exertion of EV circuits, which can be fatal for passengers. Thus, this paper proposes artificial neural network (ANN) based control strategies for enhanced speed regulation in DC motor-driven EVs. Initially, different ANN architectures namely radial basis function (RBF) neural network, nonlinear autoregressive network with exogenous inputs (NARX), nonlinear autoregressive (NAR) network, Elman network, recurrent neural network (RNN), feedforward (FF) network, and probabilistic neural network (PNN) are implemented to design the PID. Of these, it is identified that the FF network is the best choice to design the PID based on its superior time-domain performance index. Further, the efficacy of this proposed ANN-PID controller is compared with the conventional Fuzzy-PID controller subjected to various disturbances namely sine, ramp, step, and chirp. The transient response and steady-state response simulation results proved that the proposed ANN-PID controller delivers superior performance compared to the conventional Fuzzy-PID controller.