Implementation of nonlinear model predictive control of magnetic levitation laboratory plant

Abstract Nowadays, predictive control methods are very popular and are often used to control various systems. Linear predictive control techniques are relatively well discovered and standardised, but this is not true for predictive control methods used for nonlinear systems, which are far more frequent in nature. There is no universal approach to handle it. Nonlinear model predictive control (NMPC) is an extension of the linear model predictive control (MPC) method, which is widely used for solving nonlinear control problems. In this paper, a real-time adaptation of linear MPC for nonlinear systems is presented. This method uses nonlinear model dynamics with the advantage to provide a precise prediction of the system response to initial conditions, known future and estimated progress, and an inside-one-step evolving linearised discretised nonlinear model for predicting response to estimated control input deviance used for optimisation. The functionality of the proposed method is shown using MATLAB simulation and demonstrated by experiment on a fast nonlinear magnetic levitation plant. The proposed controller is compared with a simple control loop with PID with better control results of NMPC at the cost of higher computational complexity. In this work, we present the NMPC method that collects many interesting findings from other authors who dealt with predictive controllers and adds our knowledge, which leads to a standardised predictive control method suitable for controlling linear or nonlinear systems with state-space representation.