Stochastic Optimization of PID Parameters for Twin Rotor System with Multiple Nonlinear Regression

This paper demonstrates finding the PID controller parameters which are used to acquire the desired settling time of the horizontal and vertical position variables of the Twin Rotor experiment setup. Modeling and optimization processes have been achieved based on multiple nonlinear regression analysis and Differential Evolution algorithm, respectively. In the modeling part a hybrid approach combining the ideas of Artificial Neural Network and regression have been introduced. For this process, training and test phases are executed on Mathematica environment with constraints on settling time, rising time and maximum overshoot. In order to provide comparison of the output parameters, the PID parameters based on the Ziegler-Nichols tuning method is used. The experimental performance of the system is tested, thus an appropriate set of methods for tuning PID parameters is introduced.