An Efficient Single Neuron PID — Sliding Mode Tracking Control for Simple Electric Vehicle Model - Shamseldin, Mohamed A. (Journal of Applied Nonlinear Dynamics, 2026)
The paper presents a new scheme for sliding mode control (SMC) using single neuron PID (SNPID) to treat the shuttering signal output of SMC. This study develops a modified technique based on the combination of the SNPID, as a main controller and SMC, as an adaptation technique, to design an optimized self-tuned for SNPID controller that may overcome difficulties faced when a change in system operating points occurs. The purpose of the proposed controller is to track the reference speed of the electric vehicle (EV). A steady MATLAB/Simulink model was established and validated. It was then used to estimate the system performance. The optimal parameters of the proposed controller were obtained using the harmony search optimization based on an effective cost function. The simulation assumes a DC permanent magnet motor, ideal mechanical transmission. Two tests were executed, the first test was implemented at fixed reference speed while the second test was subjected at several commands of reference speed. The SNPID-SMC has been compared to the PID controller to ensure robustness. The obtained results can be summarized as follows. In the first test, The SNPID-SM controller reaches the steady-state speed at 8.8378 seconds while the PID controller stabilizes at 15.4530 seconds. Also, the SNPID-SM controller has a 0.06 % steady-state error however, the PID controller has a 4% steady-state error. Moreover, the settling time of SNPID-SMC is 14.4699 while the PID controller is 28.6823. Besides, the second test shows that the SNPID-SM controller can minimize the mean square by a percentage of 28.12 % compared to the PID controller. Lastly, the proposed SNPID-SM controller can enhance the dynamic response of EV significantly.