PID2018 Benchmark Challenge:Multi-Objective Stochastic Optimization Algorithm

TL;DR

This paper introduces a modified stochastic multi-objective optimization algorithm to tune PI controllers in refrigeration systems, improving control performance and reducing steady-state error through a novel conditional integral structure.

Contribution

The paper proposes a modified SMDO algorithm for multi-objective controller tuning, incorporating a conditional integral structure to enhance steady-state performance.

Findings

Improved control performance demonstrated through simulations.

Reduction in steady-state error achieved.

Comparison shows advantages over existing methods.

Abstract

This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions.