Model Predictive Control for Cooperative Rendezvous of Autonomous Unmanned Vehicles

This thesis investigates cooperative maneuvers for aerial vehicles autonomously landing on moving platforms. The objective has been to develop methods for safely performing such landings on real systems subject to a variety of disturbances, as well as physical and computational constraints. Two specific examples are considered: the landing of a fixed-wing drone on top of a moving ground carriage; and the landing of a quadcopter on the deck of a boat. The maneuvers are executed in a cooperative manner where both vehicles are allowed to take actions to reach their common objective, while avoiding safety based spatial constraints. Applications of such systems can be found in, for example, autonomous deliveries, emergency landings, and in search and rescue missions. Particular challenges of cooperative landing maneuvers include the heterogeneous and nonlinear dynamics, the coupled control, the sensitivity to disturbances, and the safety criticality of performing a high-velocity landing maneuver. In this thesis, a cooperative landing algorithm based on Model Predictive Control (MPC) that includes spatial safety constraints for avoiding dangerous regions is developed. MPC offers many advantages for the autonomous landing problem, with its ability to explicitly consider dynamic equations, constraints, and disturbances directly in the computation of the control inputs. It is shown that the cooperative landing MPC can be decoupled into a horizontal and a vertical sub-problem. This result makes the optimization problems significantly less computationally demandingand facilitates the real-time implementation. The autonomous landing maneuver is further improved by the employment of a variable horizon. The variable-horizon MPC framework lets the finite horizon length become a part of the optimization problem, and makes it possible to always extend the horizon to the end of the landing maneuver. An algorithm for variable horizon MPC that can be implemented to real-time systems is derived by the use of efficient update rules, and by taking into account the similarities between the multiple optimization problems that we have to solve in each sampling period. The algorithm is fast enough to be used even in time-critical systems with long horizons. Furthermore, the solution time of the variable-horizon MPC decreases as the target gets closer. This means that the computational demand becomes smaller in the most critical part of the landing maneuver. The algorithms are derive