Evaluation Of Varied Model Order In Ga-Optimised Parameter Estimation Of Toothbrush Rig System

Parameter estimation is a vital part in constructing the best model of a dynamic system. This paper analyzed the performance of toothbrush rig parameter estimation using different model orders. Parameter estimation process of the system is performed through system identification. The approximate mathematical model that resembles the real system is obtained when the output is measured after loading the input signal. The application of real-coded genetic algorithm (RCGA) is proposed as optimization method in estimating the parameters of dynamic system. The best model is obtained by optimizing the objective function of mean squared errors. The performance is analyzed to get the approximate model of the real system using three different model orders with 10 times analysis for each model. A few criteria have been considered which are the optimization result of objective function, time execution and validation process. The estimated parameters are acceptable and possible to be used for controller development later on. Estimated parameter with model order 3 is chosen as the best model or the dynamic system as it has the highest performance compared to others.