ABSTRACT
Recently, model-predictive control (MPC) has been recognized as a promising method in the unmanned aerial vehicle (UAV) community. The essential procedure in the implementation of MPC algorithms is to solve the formulated optimization problem (OP). For nonlinear system, MPC technique generally requires solving an optimization problem numerically at every sampling instant, which poses obstacles on the real-time implementation due to the heavy computational burden. Although the development of the avionics and microprocessor technology makes the online optimization possible, the implementation of computationally demanding nonlinear MPC on small UAVs is very challenging. The associated low bandwidth and computational delay make it very difficult to meet the control requirement for systems with fast dynamics such as helicopters. Only a few applications on helicopter flight control have been reported in [191, 192], where the authors adopt a high-level MPC to solve the tracking problem and rely on a local linear feedback controller to compensate the high-level MPC. Moreover, the formulated nonlinear optimization
problem has to be solved by a secondary flight computer. The extra payload and power consumption are unsuitable for a small-scale helicopter.
