Model Reference Adaptive Control Design for Slow Processes. A Case Study on Level Process Control

The design of controllers, using conventional techniques, for plants with nonlinear dynamics or model uncertainties can be often quite difficult. Model reference adaptive control (MRAC) is a modern alternative for classic control algorithms and a convenient method to updating the controller's parameters for slow processes with parameter variations. In this paper is presented a comparative study of two design procedures of MRAC for first order slow processes, particularly applied and tested on level process control. The first method it is based on the MIT rule and the second one it is based on the stability theory of Lyapunov. The theoretical results are obtained using Matlab simulation environment.