MPC-Based UAV Navigation for Simultaneous Solar-Energy Harvesting and Two-Way Communications

The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV’s flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users’ minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches.