Self-tuning PID control scheme with swarm intelligence based on support vector machine

This paper presents a self-tuning PID control scheme based on support vector machine (SVM) and particle swarm optimization (PSO). Due to its excellent ability in nonlinear function approximation, support vector machine is used to identify the dynamics of the turbine engine. Using the SVM model, the future outputs of the engine are predicted, which are then used to formulate an objective function. The PID parameters are tuned by minimizing the objective function using PSO. The proposed control scheme is simulated in the MATLAB environment for the engine control. The results demonstrate that the controller can achieve robust control of the engine with good performance in tracking reference trajectory.