Abstract
This article presents an optimization-based control framework for the autonomous forced landing of a fixed-wing unmanned aircraft (UA). A two-level model-predictive control (MPC) scheme is proposed to realize this framework, where an economic model-predictive control (EMPC) in a long piecewise constant fashion is proposed at the high level, while a short fixed-horizon linear time-varying MPC at the low level responds to fast dynamics of UA and tracks the optimal path provided by the high-level controller, alleviating computational burden compared to the high-frequency single-layer MPC scheme. Compared with a single EMPC setup with high sampling frequency, this hierarchical EMPC controller can significantly reduce the computational complexity and make it feasible to be implemented in real time. In addition, it also responds to disturbances (e.g., wind) and aircraft fast dynamics in a timely manner. The recursive feasibility and stability of the high- and low-level MPC schemes are established. The performance of the proposed EMPC forced landing function is illustrated by simulation case studies on both Aerosonde and Skywalker X8, compared favorably with competing schemes.
Highlights
I N recent years, Unmanned Aircraft (UA) has received considerable attention and development in both military and civilian applications due to its unique features: UA can significantly reduce the cost for training skilled pilots and guarantee human pilots’ safety for long-range remote sensing, delivery and surveillance missions in dangerous areas.without pilots, many safety issues arise
With an increased presence of UA over civilian populations and infrastructure assets, a reliable and robust system for forced landing is crucial in the deployment of UA systems to reduce the risk of casualties and asset losses
2) A two-level MPC scheme is proposed to implement this framework in trading off between many key factors, e.g. computational complexity, optimality, fast dynamics and disturbance rejection. In this implementation, based on a simplified dynamic model, the high-level Economic Model Predictive Control (EMPC) adopts shrinking horizon, piecewise constant control and pure economic cost function so that it significantly reduces the workload of the online optimization; a fast linear time-varying MPC within a fixed horizon has been designed in order to reduce the effect of disturbances and uncertainties using its inherent robustness2 With the optimal control actions determined by the two-level controller, numerical implementations have been performed on a high-fidelity 6 degree-of-freedom aircraft model
Summary
I N recent years, Unmanned Aircraft (UA) has received considerable attention and development in both military and civilian applications due to its unique features: UA can significantly reduce the cost for training skilled pilots and guarantee human pilots’ safety for long-range remote sensing, delivery and surveillance missions in dangerous areas. 2) A two-level MPC scheme is proposed to implement this framework in trading off between many key factors, e.g. computational complexity, optimality, fast dynamics and disturbance rejection In this implementation, based on a simplified dynamic model, the high-level EMPC adopts shrinking horizon, piecewise constant control and pure economic cost function so that it significantly reduces the workload of the online optimization; a fast linear time-varying MPC within a fixed horizon has been designed in order to reduce the effect of disturbances and uncertainties using its inherent robustness With the optimal control actions determined by the two-level controller, numerical implementations have been performed on a high-fidelity 6 degree-of-freedom aircraft model.
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