Uncertainty Management for Aerial Vehicles: Coordination, Deconfliction, and Disturbance Rejection
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The presented dissertation aims to develop control algorithms that deal with three types of uncertainties managements. First, we examine the situation when unmanned aerial vehicles (UAVs) fly through uncertain environments that contain both stationary and moving obstacles. Moreover, a guarantee of collision avoidance is necessary when UAVs operate in close proximity of each other. Second, we look at the communication uncertainty among the network of cooperative UAVs and the efforts to establish and maintain the connectivity throughout their entire missions. Third, we explore the scenario when the aircraft flies through wind gust. The introduction of an appropriate control scheme to actively alleviate the gust loads can result into weight reduction and consequently lower the fuel cost. In the first part of this dissertation, we develop a deconfliction algorithm that guarantees collision avoidance between a pair of constant speed unicycle-type UAVs as well as convergence to the desired destination for each UAV in presence of static obstacles. We use a combination of navigation and swirling functions to direct the unicycle vehicles along the planned trajectories while avoiding inter-vehicle collisions. The main feature of our contribution is proposing means of designing a deconfliction algorithm for unicycle vehicles that more closely capture the dynamics of constant speed UAVs as opposed to double integrator models. Specifically, we consider the issue of UAV turn-rate constraints and proceed to explore the selection of key algorithmic parameters in order to minimize undesirable trajectories and overshoots induced by the avoidance algorithm. The avoidance and convergence analysis of the proposed algorithm is then performed for two cooperative UAVs and simulation results are provided to support the viability of the proposed framework for more general mission scenarios. For the uncertainty of the UAV network, we provides two approaches to establish connectivity among a collection of UAVs that are initially scattered in space. The goal is to find shortest trajectories that bring the UAVs to a connected formation where they are in the range of detection of one another and headed in the same direction to maintain the connectivity. Pontryagin Minimum Principle (PMP) is utilized to determine the control law and path synthesis for the UAVs under the turn-rate constraints. We introduce an algorithm to search for the optimal solution when the final network topology is specified; followed by a nonlinear programming method in which the final configuration is emerged from the optimization routine under the constraints that the final topology is connected. Each method has its own advantages based on the size of corporative networks. For the uncertainty due to gust turbulence, we choose a model predictive control (MPC) technique to address gust load alleviation (GLA) for a flexible aircraft. MPC is a discrete method based on repeated online optimization that allows direct consideration of control actuator constraints into the feedback computation. Gust alleviation systems are dependent on how the structural flexibility of the aircraft affects its dynamics. Hence, we develop a six-degree-of-freedom flexible aircraft model that can integrate rigid body dynamic with structural deflection. The structural stick-and-beam model is utilized for the calculation of aeroelastic mode shapes and airframe loads. Another important feature of MPC for GLA design is the ability to include the preview of gust information ahead of the aircraft nose into the prediction process. This helps raising the prediction accuracy and consequently improves the load alleviation performance. Finally, the aircraft is modified by the addition of the flap-array, a composition of small trailing edge flaps throughout the entire span of the wings. These flaps are used in conjunction with the distributed spoilers. With the availability of the control surfaces closer to the wing root, the MPC with flap-array can reduce the wing bending moment from different mode shapes and achieve better load alleviation performance than the original aircraft.