Thermoelectric cooler identification based on continuous-time Hammerstein model using metaheuristics algorithm

Abstract

This paper presents the identification of the Thermoelectric Cooler (TEC) plant using a novel metaheuristic called hybrid Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based continuous-time Hammerstein model. In the identification, a continuous-time linear system is used, which is more suitable for representing any real plant. The hMVOSCA algorithm is used to reduce the gap between estimated and actual output by identifying the coefficients of both the linear and the nonlinear Hammerstein model subsystems. Efficiency of the hMVOSCA algorithm also evaluated based on the convergence curve, bode plot of the linear subsystem, function plot of the nonlinear subsystem, and statistical performance value. The results demonstrate that the proposed hMVOSCA algorithm can produce the Hammerstein model that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA algorithm outperforms the conventional metaheuristic algorithms such as MVO and SCA by balancing exploration and exploitation and low searching capability.