Experimental Validation of an enhanced Optimal Adaptive Control Scheme using Dominant Poles for a Variable Area Process

Abstract

This paper describes an efficient control scheme to enhance the adaptive control in real time and an approach to Optimal Model Reference Adaptive Control (OMRAC). The conventional MRAC method has its difficulty in choosing the reference model and adaption gain ‘?’. An OMRAC adaptive controller is used to identify automatically the process dynamics. The selection of reference models in OMRAC scheme is based on the Multiple Models (MM) depending on the operating regime of the process. In this proposed work, the reference model considered is a second order system having fixed roots in denominator polynomial representing the most dominant poles of the process, determined using Dominant Pole Algorithm (DPA). The adaptive controller employs an optimal search algorithm for tuning ‘?’ using Particle Swarm Optimization (PSO), that best fits the observed response. Experimental validation is performed in a variable area conical tank process and comparative performance evaluations are analyzed for both conventional gain scheduled PID and proposed OMRAC method. The result gives consistently better setpoint tracking mechanism and error minimization.