This paper focuses on designing control systems for the regulation of unmanned aerial vehicles (UAVs) along real-time trajectories. The reference trajectories will be generated in real-time from a library of pre-specified motion primitives. Two control approaches will be studied. The first is a hybrid control approach, in which we account for all possible connections between compatible library primitives by adding corresponding coupling conditions into the control synthesis program. The switching between primitives in this case takes place with stability and performance guarantees, but at the expense of added computational complexity. The second approach is a decoupled control approach, where the plant associated with each primitive is regarded as a system with an uncertain initial state. This approach is less computationally intensive than the hybrid approach, but comes with no theoretically established stability guarantees across switching boundaries. The two approaches are applied to regulate a nonlinear mathematical model of a 6 foot Telemaster fixed-wing UAV along a real-time trajectory in the presence of model uncertainties and atmospheric disturbances.
- Dynamic Systems and Control Division
Optimal Control of Fixed-Wing UAVs Along Real-Time Trajectories
Arifianto, O, & Farhood, M. "Optimal Control of Fixed-Wing UAVs Along Real-Time Trajectories." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 205-214. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8869
Download citation file: