Estimation of parameters of a laboratory attitude control simulator using least squares method and hybrid intelligent optimization

In this paper, the parameters of the dynamic model of a three-degree-of-freedom simulator based on air bearing, including moment of inertia, center of mass, using experimental data in a maneuver in two ways: 1- hybrid optimization method of genetic algorithm and second-order programming and 2- The method of least squares error is estimated. To do this, a position maneuver is performed using reaction wheels, and torque values as well as angular velocities around three axes are recorded. The table parameters are then estimated using the stored data and the implementation of the two methods. The results show that with the least squares method, unlike the hybrid optimization algorithm, with a attitude control test from the non-zero initial point to the origin, it is not possible to derive a good estimate and different tests are needed to stimulate all system modes. While in the hybrid optimization method, by performing the same experiment, the desired results of the system estimation can be presented. To validate the implemented algorithm, the performance of the closed loop of the table in the laboratory environment and the simulated model were evaluated and compared, which indicates the appropriate accuracy (less than 5% error) of the estimation methods.