Active Model-Based Predictive Control and Experimental Investigation on Unmanned Helicopters in Full Flight Envelope

For the control of unmanned helicopters in full flight envelope, an active model based predictive control scheme is developed in this brief. Dynamics in full envelope is modeled, with uncertainties represented by the system model error and process noise. The model error depends on both helicopter dynamics and flight mode, and the process noise is assumed unknown but bounded. Based on the set-membership filter, an active modeling based stationary increment predictive control, based on the estimated model error and its boundary to optimally compensate the model error, as well as the aerodynamics time delay, is proposed. The proposed method has been implemented on the ServoHeli-40 unmanned helicopter platform and experimentally tested; the results have demonstrated its effectiveness.