Optical EngineeringIdentification of electro-optical tracking systems using genetic algorithms and nonlinear resistance torque
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Electro-optical (EO) tracking systems, while exhibiting strong nonlinear characteristics, are difficult to accurately model. Nonlinear resistance torque is proposed to describe the system’s nonlinear phenomenon and the genetic algorithm is used to identify model parameters. The model’s root-mean-square error (RMSE) was reduced using nonlinear resistance torque by 2.5 times compared to the Stribeck friction model and by 12 times compared to the linear model. Under the identified model, the system’s nonlinearity was effectively compensated. The results demonstrate the feasibility of the proposed method for the identification of EO tracking systems.