Metamodeling approach for PID controller optimization in an evaporator process

This paper aims to describe Metamodeling approach, a technique that can be utilized to tune controller parameters for a non-linear process quickly, and how it is used to solve real world engineering problems by applying it to the problem of designing a proportional-integral-derivative (PID) controller. The process used in this study is a single input single output (SISO) evaporator system which is intrinsically non-linear plant. The Radial Basis Function Neural Network Metamodel used was able to give a good approximation to the optimum controller parameters in this case. Also Genetic Algorithm (GA) and Ant Colony Optimization (ACO) are used to design the controller parameters and are compared with Metamodeling.