Multimodel approach to the design of scheduling controllers for a class of nonlinear system

Nonlinearity emerges due to parameter uncertainties, constraints and also due to the fact that processes are subjected to disturbances quite often. This nonlinearity behavior leads to operate the industrial processes at different regions. These problems can be solved effectively by implementing a controller based on multimodel approach. In order to design such controllers it is always mandatory to properly identify the process. In our work the chosen process is identified using real time data acquired from the laboratory level process station. Though the design of constant gain controllers are the most popular option to solve the industrial control problems, but it fails to show better results for nonlinear systems. This paper aims at developing design strategy of scheduling controller by extending Polynomial Dynamic Control approach. The parameters of scheduling controller are self tuned in order to accommodate the system uncertainties. The goal of the paper is to analyze the reference tracking efficiency and external disturbance rejection when implementing these two approaches in controlling the level under different operating regions. Simulation studies carried out on MATLAB/SIMULINK platform reveal that the polynomial controllers can effectively be employed for multimodel approach in case of nonlinear systems.