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This thesis investigates the coordination and control of fleets of unmanned aerial vehicles (UAVs). Future UAVs will operate autonomously, and their control systems must compensate for significant dynamic uncertainty. A hierarchical approach has been proposed to account for various types of uncertainty at different levels of the control system. The resulting controller includes task assignment, graph-based coarse path planning, detailed trajectory optimization using receding horizon control (RHC), and a low-level waypoint follower. Mixed-integer linear programming (MILP) is applied to both the task allocation and trajectory design problems to encode logical constraints and discrete decisions together with the continuous vehicle dynamics. The MILP RHC uses a simple vehicle dynamics model in the near term and an approximate path model in the long term. This combination gives a good estimate of the cost-to-go and greatly reduces the computational effort required to design the complete trajectory, but discrepancies in the assumptions made in the two models can lead to infeasible solutions. The primary contribution of this thesis is to extend the previous stable RHC formulation to ensure that the on-line optimizations will always be feasible. Novel pruning and graphsearch algorithms are also integrated with the MILP RHC, and the resulting controller is analytically shown to guarantee finite-time arrival at the goal. This pruning algorithm also significantly reduces the computational load of the MILP RHC. The control algorithms acting on four different levels of the hierarchy were integrated and tested on two hardware testbeds (three small ground vehicles and a hardware-in-theloop simulation of three aircraft autopilots) to verify real-time operation in the presence of real-world disturbances and uncertainties. Experimental results show the successful control loop closures in various scenarios, including operation with restricted environment knowledge based on a realistic sensor and a coordinated mission by different types of UAVs. Thesis Supervisor: Jonathan P. How Title: Associate Professor 3

Distributed Receding Horizon Control with Priority for Multi Robot System Including Ad-hoc Network