Real-Time Identification and Self-Tuning PID Controller for 1-DOF Helicopter System

Obtaining an accurate mathematical model is critical in control design applications. Therefore, the parameter uncertainties that occur during modeling or operation should be identified, and the controller design should be made to compensate for these uncertainties. In this paper, a system identification study was performed with a global-based Big Bang-Big Crunch optimization algorithm using input and output data from the one degree of freedom helicopter system. System dynamics are determined using this optimization algorithm over the flowing data, and the transfer function that best represents the system's behavior is obtained. A discrete time Proportional Integral Derivative (PID) controller is designed to minimize the overshoot and steady-state error of the system response with the same optimization algorithm. This process was used to automatically detect parametric uncertainties as long as the system receives input-output data, and to create a tuned PID controller structure. In order to demonstrate the superiority of the proposed methodology, initial PID is designed for the real-time system, and the performance of this PID and the tuned PID controller is compared under the parameter uncertainty. As a result, it is ensured that the tuned PID structure has the desired transient response dynamics of the system under parameter uncertainty compared to the initial PID structure.