Industrial Controller Design Using Optimization Techniques Subjected to Multiple H∞ Criteria

Abstract

This paper presents a study on optimization techniques for tuning industrial controllers. To this end, a multi-criteria optimization problem H∞ was formulated by analyzing the desired behavior of the closed-loop system for low, medium, and high-frequency ranges. For each criterion, constraints are specified according to stability and performance specifications of the process. To perform optimization subject to the nonlinear constraints of controller parameters, Sequential Quadratic Programming (SQP) and Genetic Algorithm (GA) methods were employed. To evaluate the controllers, simulations were conducted using a typical plant model, and metrics such as IAE, ISE, and ITAE were calculated. Finally, optimization of a controller for a temperature process was performed.