A stochastic robustness controller for a saucer-shaped UAV based on quantum evolutionary algorithm

A Lifting body unmanned aerial vehicle (UAV) generates lift by its body and is widely used due to its significant advantages. However, designing the control law for a lifting body UAV is quite challenging because it has strong nonlinearity and coupling, and is usually lack of rudders. In this paper, a stochastic robustness control strategy based on quantum evolutionary algorithm (QEA) is proposed to design a control law for a saucer-shaped UAV which can be adequately modeled with a rigid 6-degrees-of-freedom (DOF) representation. Considering the inaccurate parameters of the saucer-shaped UAV, this method uses Monte Carlo simulation (MCS) to evaluate the stochastic response performance of the system and a hybrid QEA with 2-crossovers to optimize the parameters in the controller. The overshoot and rise time of the stochastic response are chosen as optimization indexes. Compared with traditional controller, this method has strong robustness and show good control performance. The simulation results show that the stochastic robustness control method is a good way to deal with the control problem of lifting body UAVs and also can be used in other control systems.