Multi-Model Predictive Control (MMPC) for Non-linear Systems with Time Delay: An Experimental Investigation

This paper depicts an experimental validation and comparison of gap metric based weighting methods for nonlinear processes with delay using Multi-model predictive control (MMPC). Controlling nonlinear processes is a difficult task and the difficulty increases when there is time delay in the process. Multi model technique is the simplest approach and is used to control the nonlinear process from decades. In this research, model predictive control is developed in a multi model framework (MMPC). In MMPC the global controller formation depends on local weights and these weights are calculated based on gap metric. From the literature, we found two popular weighing functions based on gap metric. Here we present the comparative performance analysis of those weighing functions through simulation and experimental studies. Level control in a conical tank process is cosnidered for experimental implementation of the considered weighting methods in MMPC framework.