The objective of this effort is to create potential commercial flight control products in the emerging eVTOL / UAM vehicles market. The focus will be on the development of eVTOL vehicles autonomy and distributed electric propulsion (DEP)-based control techniques. eVTOL vehicles with lift production from wings and distributed propulsive system present unique control challenges since each electric motor in these systems can be a source of lift, thrust, and actuation. Improving the flexibility, reliability, and performance of electric propulsion systems is crucial for safe and optimum operation of eVTOL / UAM vehicles in both military and civil sectors. Distribution of electrically-powered propulsors across a flying vehicle is used to provide both the required thrust for flight, as well as additional advantages associated with synergistic propulsion-airframe integration. Existing control methods tend to use techniques designed for fixed-wing-aircraft or multicopters and do not provide the possibility to fully utilize the DEP system benefits. Distributed decentralized model predictive control (MPC) is one of the approaches of interest for this problem. In flying vehicles, the propulsion, power, and flight control systems are compactly coupled and interacting with each other at multiple time scales. In eVTOL flight control system, besides DEP control functionality, energy optimization is necessary to improve energy efficiency, and dynamic power management is needed to achieve better performance and power quality. We propose distributed decentralized model predictive flight control (MPFC) system to address these challenges. By utilizing propulsion-based MPFC, (a) the distributed nature of propulsors can be used to provide control assurance under critical faults and failures of other systems for vehicle control, (b) limits of the dynamical systems can be handled more efficiently and the eVTOL vehicle can operate closer to its constraints and hence the flight envelope can be expanded, and (c) thermal / power / energy management challenges can be addressed optimally.
