Cascade attitude control for nonminimum phase lifting reentry vehicles

This paper proposes a cascade attitude controller combining the dynamics inverse control based on disturbance observer and model predictive control, for studying the lifting reentry vehicles with nonminimum phase characteristic. To solve the output tracking problem of these lifting reentry vehicles, a cascade control strategy is developed in which the external inputs control the external states and the external state deviations control the internal states. In the internal state loop, a model predictive controller with optimality is used to stabilize the internal dynamics via a smaller control quantity; in the external state loop, a dynamics inverse controller is used to keep the external dynamics deviation to the control quantity that the inner loop needs, and a disturbance observer is added to eliminate the modeling uncertainty in the reentry phase, so that the vehicle attitude can track the output command stably. Simulation results show that this cascade controller can solve the attitude tracking problem of the nonminimum phase system. Monte Carlo results show that this cascade controller is of good robustness in the case of modeling uncertainty.