Hybrid Optimization of Controller for Multi-variable System

The majority of industrial processes are multivariable in nature. Because of changes in process dynamics and interactions between process variables, controller design for the Multi Input Multi Output process is complicated. The main objective of this paper is to design a hybrid optimizations algorithm for the control of multivariable systems. This research aims to develop an optimized Iterative Learning Controller based PI controller for a quadruple tank process tuned through Ant Lion Optimization and a Simulated Annealing Algorithm. The quadruple-tank process is commonly used in a chemical engineering process control application. For a different set of test functions, its output has been compared to a few other well-known algorithms of the same kind. The proposed system's validity and robustness were assessed using simulation results. The multivariable method achieves good set-point tracking and disturbance attenuation performance.