Learning and H∞ control of an overhead crane for obstacle avoidance and disturbance rejection

A control strategy is developed for a 3-dimensional overhead crane. Using the differential flatness of the crane and a parameterization method, we first calculate the optimal trajectory of the payload that results in minimum transfer time when there exist obstacles in the direct moving path. A learning algorithm is then used to generate the desired feed forward input signal that can drive the system output to track the optimal trajectory. A key feature of this strategy is that it is model free and thus is robust to uncertainties in modeling and parameters. When there exist external disturbances, an H _∞ optimal control method is used to reject the disturbances. Simulation results are given to verify the strategy and compare some performances