In this paper, we investigate the feasibility of a nonlinear model predictive tracking control (NMPTC) for autonomous helicopters. We formulate a NMPTC algorithm for planning paths under input and state constraints and tracking the generated position and heading trajectories, and implement an on-line optimization controller using gradient-descent method. The proposed NMPTC algorithm demonstrates superior tracking performance over conventional multi-loop proportional-derivative (MLPD) controllers especially when nonlinearity and coupling dominate the vehicle dynamics. Furthermore, NMPTC shows outstanding robustness to parameter uncertainty, and input saturation and state constraints are easily incorporated. When the cost includes an potential function with a possibly moving obstacle or other agents' state information, the NMPTC can solve the trajectory planning and control problem in a single step. This constitutes a promising one-step solution for trajectory generation and regulation for RUAVs, which operate under various uncertainties and constraints arising from the vehicle dynamics and environmental contingencies. The computation load of this approach is significantly less than many existing model predictive algorithms, thus enabling real-time applications for autonomous helicopters.
Kim et al. REF investigated the feasibility of a nonlinear MPC in the control of unmanned helicopter in 2002.
Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)
Computer Science