Speed Control of DC Servo Motor Under Comparison with PID Tuner Control and Neural Network Control Using Simulink and ESP32

Abstract

In this article, a comparison of new adaptive control methods optimized under the Matlab PID Tuner design tool and the Neural Network tool is demonstrated. These methods led to controlling the excitation separately from the TRE-TS3252 DC servomotor, for feedback speed control under Kirchhoff's law. Said servomotor is part of a robotic system, and due to its obsolescence of both the DC servomotor and the manufacturer's controller, it requires readaptation of a new control system. These modern techniques improve the speed responses of the DC servo motor, using the ESP32 micro controller and the BTS7960 driver controller. Results were obtained in real-time simulation to validate the influence and performances of the PID Turner and Neural Network control methods, giving dynamic behavior parameters obtained from the Neural Network controller more acceptable compared to the PID Turner controller.