Hierarchical model predictive control of UAVs performing multitarget-multisensor tracking

Detection and tracking of enemy emitters such as radar-carrying platforms is a task of considerable military significance. In the work presented here, the problem of adaptively controlling the trajectories of an autonomous team of unmanned aerial vehicles (UAVs) performing this task, in order to minimize emitter localization error, while simultaneously avoiding no-fly zones, is considered and a solution developed. Because of the computational complexity of the problem when long-term goals are considered, an optimal solution cannot be found in practice by any physically implementable method. Hence, in this paper an approach is developed that enables implementation of a computationally feasible, suboptimal solution that takes into account both short-term and long-term goals. To this end, the problem is addressed by developing a new hierarchical model predictive control (MPC) algorithm. To evaluate the effectiveness of the approach, first a controller is developed using an idealized UAV model and simulations are performed. Its performance is compared with a commonly used “myopic” control approach and found to give important improvements. Subsequently an improved planner is incorporated and tested, and then a version of the controller using a fixed-wing aircraft model for the UAVs is implemented. This version is also tested by simulation and found to perform successfully. Finally, a brief discussion on system stability is provided as part of the evaluation.