Аңдатпа
This paper presents the investigation of the process of optimizing the parameters of a PID controller using machine learning algorithms for the oil separation process control system. The optimization of the controller parameters (Kp, Ki, Kd) is important, in order to improve control quality and reduce the number of errors in dynamic processes. To solve this issue, several innovative methods were considered, such as the cuckoo search algorithm (CSA), the firefly algorithm (FA), particle swarm optimization (PSO), and the support vector machine (SVM). All the data, including the current process values (PV), setpoints (SP) and output signals (OP) were obtained from Tengizchevroil. In addition, the metrics, such as root-mean-square error (MSE), adjustment time, overshoot, and steady-state error were used to assess the effectiveness of optimized regulators. Overall, the results of the research indicate that there was a significant improvement of the dynamic characteristics of the system due to the usage of machine learning algorithms compared to the traditional approaches. The obtained parameters of optimization achieved the target value while being faster and more stable, thus increasing the productivity of control in the technological process.