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In this paper, we investigate the problem of robust altitude stabilization for the vertical take-off and landing (VTOL) of an unmanned aerial vehicle in the presence of external disturbances (e.g. wind gusts), payload variations, and noisy measurements. The design of the controller is simple; it is based on the minimization of the one-step-ahead predicted position errors. The stability analysis of the closed loop in the presence of external disturbances is presented. The analysis results prove that the tracking errors of attitude, take-off, hovering, and landing are uniformly bounded. Since the proposed control algorithm will be employed for goods delivery by drones, the robustness of this algorithm against low-frequency disturbances and payload variations is a major objective. To this end, integral action is included in the altitude loop to eliminate the induced steady-state error and to drop off the payload in the desired position successfully. Furthermore, the controller is given in the closed form to facilitate its implementation onboard to increase the autonomy of the flight. The numerical simulations are provided to show the effectiveness of the proposed algorithm.

This document is reference [14] cited in the paper:S. Zhao, W. Dong, and J. A. Farrell, "Quaternion-based Trajectory Tracking Control of VTOL-UAVs using Command Filtered Backstepping," American Control Conference, pp. 1018-1023, June 2013. DOI: 10.1109/ACC.2013.6579970.This technical report contains more details than the conference paper. Please cite the ACC paper rather than this technical report.

The interest in electric and hybrid electric power system has been increasing, in recent times, due to the benefits of this technology, such as high power-to-weight ratio, reliability, compactness, quietness, and, above all, elimination of local pollutant emissions. One of the key factors of these technologies is the possibility to exploit the synergy between powertrain, structure, and mission. This investigation addresses this topic by applying multi-objective optimization to two test cases—a fixed-wing, tail-sitter, Vertical Take-off and Landing Unmanned Aerial Vehicle (VTOL-UAV), and a Medium-Altitude Long-Endurance Unmanned Aerial Vehicle (MALE-UAV). Cruise time and payload weight were selected as goals for the first optimization problem, while fuel consumption and electric endurance were selected for the second one. The optimizations were performed with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and S-Metric Selection Evolutionary Multiobjective Algorithm (SMS-EMOA), by taking several constraints into account. The VTOL-UAV optimization was performed, at different levels (structure only, power system only, structure and power system together). To better underline the synergic effect of electrification, the potential benefit of structural integration and multi-functionalization was also addressed. The optimization of the MALE-UAV was performed at two different levels (power system only, power system, and mission profile together), to explore the synergic effect of hybridization. Results showed that large improvements could be obtained, either in the first test case when, both, the powertrain design and the aircraft structure were considered, and in the optimization of the hybrid electric UAV, where the optimization of the aircraft flight path gave a strong contribution to the overall performances.

The interest in electric and hybrid electric power system has been increasing, in recent times, due to the benefits of this technology, such as high power-to-weight ratio, reliability, compactness, quietness, and, above all, elimination of local pollutant emissions. One of the key factors of these technologies is the possibility to exploit the synergy between powertrain, structure, and mission. This investigation addresses this topic by applying multi-objective optimization to two test cases—a fixed-wing, tail-sitter, Vertical Take-off and Landing Unmanned Aerial Vehicle (VTOL-UAV), and a Medium-Altitude Long-Endurance Unmanned Aerial Vehicle (MALE-UAV). Cruise time and payload weight were selected as goals for the first optimization problem, while fuel consumption and electric endurance were selected for the second one. The optimizations were performed with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and S-Metric Selection Evolutionary Multiobjective Algorithm (SMS-EMOA), by taking several constraints into account. The VTOL-UAV optimization was performed, at different levels (structure only, power system only, structure and power system together). To better underline the synergic effect of electrification, the potential benefit of structural integration and multi-functionalization was also addressed. The optimization of the MALE-UAV was performed at two different levels (power system only, power system, and mission profile together), to explore the synergic effect of hybridization. Results showed that large improvements could be obtained, either in the first test case when, both, the powertrain design and the aircraft structure were considered, and in the optimization of the hybrid electric UAV, where the optimization of the aircraft flight path gave a strong contribution to the overall performances. [ABSTRACT FROM AUTHOR]