Disturbance observer-based backstepping control of tail-sitter UAVs

The application scope of unmanned aerial vehicles (UAVs) is increasing along with commensurate advancements in performance. The hybrid quadrotor vertical takeoff and landing (VTOL) UAV has the benefits of both rotary-wing aircraft and fixed-wing aircraft. However, the vehicle requires a robust controller for takeoff, landing, transition, and hovering modes because the aerodynamic parameters differ in those modes. We consider a nonlinear observer-based backstepping controller in the control design and provide stability analysis for handling parameter variations and external disturbances. We carry out simulations in MATLAB Simulink which show that the nonlinear observer contributes more to robustness and overall closed-loop stability, considering external disturbances in takeoff, hovering and landing phases. The backstepping controller is capable of decent trajectory-tracking during the transition from hovering to level flight and vice versa with nominal altitude drop.Web of Science106art. no. 11

Algorithms for Motion Planning and Target Capturing

This thesis addresses the development and implementation of algorithms for unmanned air vehicles (UAVs). With advances in technology, it is relatively easy to manufacture and operate UAVs that are particularly useful for dull, dirty and dangerous operations. The success of such autonomous missions depends heavily on the planning algorithms used. A key consideration in the thesis is the path planning problem for single and multiple UAV systems in obstacle rich environments in the presence of uncertainty. Recently, rapidly-exploring random trees (RRTs) have been applied to find feasible trajectories quickly in complex motion planning problems. We use RRTs to construct trees of kinematically feasible trajectories made of waypaths, and feasibility is evaluated by checking for collisions with the predicted trajectories. When there are uncertainties acting on the system, we can identify probabilistic feasible paths by growing trees of state distributions and ensuring that the probability of constraint violation is below a pre-defined value. In addition to this, a guidance law is designed combining a pursuit law with a line-of-sight law, to track the path generated by the path planner with minimum deviation. In the penultimate chapter, an application is presented where a multi-UAV system captures a more capable target by forming a target centred formation around it. The approach combines a consensus algorithm with a controller to develop a robust distributed control law for formation control. Under certain conditions, it is shown that a set of UAVs can form a target centred formation even when target information is not known. The effectiveness of this algorithm is demonstrated using numerical results. In the appendix, a decentralised scheme is described for target tracking using consensus theory in conjunction with a data fusion algorithm which guarantees perfect fault detection and isolation. This scheme was developed by the Leicester team. As part of this thesis the theoretical algorithm was tested experimentally on a set of real robots

A Probabilistically Robust Path Planning Algorithm for UAVs Using Rapidly-Exploring Random Trees

The computationally efficient search for robust feasible paths for unmanned aerial vehicles (UAVs) in the presence of uncertainty is a challenging and interesting area of research. In uncertain environments, a “conservative” planner may be required but then there may be no feasible solution. In this paper, we use a chance constraint to limit the probability of constraint violation and extend this framework to handle uncertain dynamic obstacles. The approach requires the satisfaction of probabilistic constraints at each time step in order to guarantee probabilistic feasibility. The rapidly-exploring random tree (RRT) algorithm, which enjoys the computational benefits of a sampling-based algorithm, is used to develop a real-time probabilistically robust path planner. It incorporates the chance constraint framework to account for uncertainty within the formulation and includes a number of heuristics to improve the algorithm’s performance. Simulation results demonstrate that the proposed algorithm can be used for efficient identification and execution of probabilistically safe paths in real-time