There is increasing demand for full or partial automation of autorotation maneuvers for next-generation helicopters, which may be optionally piloted or capable of fully autonomous flight. A key challenge in the development of autorotation controllers lies in the competing state constraints that often arise during the terminal, or flare, phase of the maneuver. This paper describes the development of a nonlinear model predictive control (NMPC) scheme for autorotation flare. The NMPC controller uses a nonlinear low-order model of the helicopter in autorotation to optimize the sequence of control inputs over a finite horizon. The proposed control scheme offers benefits over existing methods by balancing the simultaneous control objectives of trajectory tracking and rotor speed regulation while also requiring minimal computation time. Simulation results are presented for a six-degree-of-freedom model of the AH-1G aircraft, highlighting the benefits of the model-based control algorithm over a simpler proportional-integral-derivative control scheme. Trade studies and Monte Carlo simulations are presented that quantify the robustness of the controller to varying initial conditions, various target landing distances, and parametric error in the internal low-order model.
Low-Order Model Identification and Adaptive Observer-Based Predictive Control for Strip Temperature of Heating Section in Annealing Furnace
