Fractional-Order Nonlinear System Identification Using MPC Technique

Many real-world processes exhibit fractional-order dynamics and are described by the non-integer order differential equations. In this paper, we quantify the fitting of the Oustaloup approximation method to the fractional-order state-space system to be obtained in the specified narrow frequency range and order. A novel method of the plant model state estimation using the model predictive control (MPC) technique has been verified on the approximated fractional-order water tanks system. To improve the system tracking and reduce the experimental effort, the Kalman filter (KF) has been connected to the MPC structure. The main objective is to design a control system of the linearized fractional-order system with the tuning of its parameters concerning an additive white noise affecting the output of the system. The presented scheme has been verified using numerical examples, and the results of the prediction of the state are discussed