Identification and control of Maglev system using fractional and integer order PID controller

Abstract

System identification techniques have proved to be the most effective methodologies for the modeling highly non-linear and system. For the purpose of real-time parameter estimation of a Maglev system, a Teaching Learning Based Optimization (TLBO) for updating the weights of Functional Link Artificial Neural Network (FLANN) model is proposed and implemented in this research. Moreover, we proposed a one & two-Degree of Freedom (one-DOF & two-DOF) Fractional Order PID (FOPID) controller, where the parameters are optimized by using the Teaching Learning Based Optimization (TLBO) and the recently proposed Black Widow Optimization (BWO) algorithm. To investigate the robustness of the proposed controller, a pulse signal disturbance is added at equal intervals of the output of the identified model of the Maglev system. It is found that the suggested two-DOF FOPID controller with TLBO performs better than its counterpart in terms of both in time domain specifications (i.e., maximum overshoot = 1.2648%, settling time = 1.3884 sec and rise time = 0.8685 sec) and in robustness analysis (i.e., system is sufficiently robust, because the infinity norms of the sensitivity and the complementary sensitivity functions of the system are less than two). The TLBO algorithm has performed better for both identification and optimization of controller parameter due to very less number of algorithmic parameter is as compared to other algorithm.