PID2018 Benchmark Challenge:Multi-Objective Stochastic Optimization Algorithm
For refrigeration system control, this work proposes an incremental modification to an existing optimization algorithm for multi-objective PI tuning.
The paper modifies the SMDO algorithm for multi-objective optimization to tune PI controller parameters in vapor compression refrigeration systems, improving control performance by reducing steady-state error. Simulation results are compared with existing methods via graphical and numerical solutions.
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.