Design of Pid Controllers Using Machine Learning Technique

This work is proposing a new approach for the design of PID controllers by employing a machine learning technique called Random Forest. Conventional design of a PID controller falls into an ad hoc method by manually tuning gains, apart from the fact that it requires expertise, becomes a very time-consuming process. On the other hand, our approach taps into the predictive capability of Random Forest for finding the optimal PID gains that result in better control performance. In doing this, our methodology learns to forecast gains of PID optimally for different operating conditions by training a Random Forest model on system responses. Its efficacy will be demonstrated for a simulation case study, showing improved control performance and robustness with respect to traditional PID tuning methods. The major contributions of the paper are a machine learning-based design framework of the PID controller and application of the random forest algorithm for optimum gain selection. It has been shown that the proposed approaches improve control performance and robustness in the simulation results. This research will impact the area of Automation and Control Systems significantly, as with these approaches, faster and more accurate design of PID controllers using machine learning techniques will be possible. It can be realized through system efficiency optimization, energy use reduction, and waste minimization. It offers sustainable development in such a way that different industries are enabled to operate lucratively while causing minimal damage to the environment. Keywords—Random forest Algorithm, PID Controller, Rise time, Settling Time. By maximizing system efficiency, cutting energy use, and avoiding waste, can improve sustainability and help diverse industries run more profitably and environmentally.