Closed Loop System Identification of a DC Motor using Fractional Order Model

Abstract

Good mathematical models are vital for design of model based controllers and accurate system response predictions. Such mathematical models can be derived by employing either first principle method or empirical method. Empirical method involves model identification based on input and output data. This method is also known as system identification. Many real-time systems are inherently closed loop systems. Moreover, it is not possible to get data from an open loop system in process industry. In such cases, closed loop system identification is useful. In system identification, selection of model structure is critical. In this paper, a first order integer model and four different fractional models were identified for a DC motor in closed loop. Fractional order model parameters were optimized by minimization of sum of squared errors (SSE), using Genetic Algorithm (GA). Results show that fractional order models fit better than first order integer model. Among the four fractional models identified, the fractional model with least parameters yielded best result.