Machine learning algorithms play a vital role in enhancing the performance of industrial systems, providing high precision and operational efficiency in real time. In servo motor control systems, these algorithms help reduce noise and vibration, improving efficiency and extending equipment lifespan. This article examines various types of noise that occur and their negative impact on industrial processes. The primary research objective is to optimize PID controller parameters in servo systems using a combined algorithm that combines neural networks and genetic algorithms. Unlike traditional methods such as genetic algorithms (GA) and particle swarm optimization (PSO), which suffer from slow convergence and risk of motor damage, the proposed solution is based on a control software platform. This platform ensures safe real-time interaction with the servo motor. A CAN Bus-based control system has been developed that enables developers to: read all servo motor parameters (speed, current, voltage, encoder position); modify PID coefficients with a single click, eliminating the need for manual tuning as in MOTO-MASTER. The implementation of the developed control system allowed the use of a trained neural classifier to constrain PID parameters within safe limits, reducing search space and accelerating the optimization process. Experimental results on SPH-S servo motors demonstrated significant reduction in noise and mechanical vibrations during real-time operation while maintaining stability across a wide speed range (0-1500 rpm).
