Comparison And Applications Of Model Predictive Control And Active Disturbance Rejection Control

With the advance of the computing technology, advanced control techniques have made much progress, and model predictive control (MPC) and active disturbance rejection control (ADRC) are two representatives of such techniques. Model predictive control is a model-based control strategy which uses a plant model to predict the future outputs of the plant and introduces them in the feedback control. It has been widely used in the industrial processes. Active disturbance rejection control is a new type of practical control technology which is developed recently. The idea is to estimate the disturbance with an extended state observer and applies it in the feedback. It has been studied and applied in motion control and process control. This thesis will compare the two advanced control methods for a variety of processes, with the aim to provide design and tuning experience for these two methods.First, the basic theories of the two control methods are studied. General guidelines on parameter tuning for the two methods are provided and the performance index for comparison are explained. Then, three typical industrial processes are chosen for control design. They are the shell gasifier system with strong interaction, the hydro turbine system with non-minimum phase property, and the continuously stirred tank reactor with nonlinear and unstable properties. Control systems for the three processes are designed based on MPC and ADRC methods. The performance features of the MPC and ADRC methods in these three control processes are analyzed and summarized.