Design of Robust Crane system Using Multi-objective optimization and UKF

The Unscented Kalman filter (UKF) is the popular approach to estimate the real amount of states of dynamical systems using measurement data corrupted with noise. Also, the multi-objective uniform diversity genetic algorithm (MUGA) is one of the most famous optimization algorithms which has been used recently in a wide range of engineering multi-objective problems. In this paper, UKF filter is used to determine real states of a crane system and the MUGA algorithm is applied to achieve the optimum switching times of Bang-Bang control input based on three objective functions. Some Pareto optimum design points are presented which trade-off optimum design points can be simply selected. Simulation results show that the behavior of the obtained trade-off point is robust and the effectiveness of this algorithm is demonstrated by the cumulative distribution functions.

Keywords:

Unscented Kalman filter , multi-objective optimization , Crane