Optimal task space control design of a Stewart manipulator for aircraft stall recovery

This paper presents an algorithm to develop a mission-based optimal task space control for a Stewart manipulator. The proposed algorithm consists of two optimization phases. The first phase seeks an optimal polynomial approximate model for the forward kinematics of a Stewart manipulator using a predicted square error cost function. The second phase optimally tunes the controller gains in order to meet special mission requirements. Genetic algorithms are used in both phases as the optimization method. A stall recovery maneuver, one of the most dangerous flight conditions, is selected as the test case. The proposed mission-based optimal task space control shows the capability to reduce the error in training the pilot for stall recovery maneuver.