This thesis concerns motion planning and control of underactuated unmanned aerial and surface vehicles with special attention to disturbances.  In the first part of the thesis, we examine trajectory tracking using Prescribed Performance Control (PPC) for the classes of underactuated aerial and surface vehicles, assuming that the model parameters are unknown. Due to the underactuation, the original PPC methodology is redesigned to accommodate the specifics of the considered underactuated dynamical systems. We prove the stability of the proposed control schemes and support them with numerical simulations on the quadrotor and boat models. Furthermore, we propose enhancements to the Kinodynamic Motion Planning via Funnel Control (KDF) framework. The kinodynamic motion planning is based on the Rapidly-exploring Random Trees (RRT) algorithm, and our improvements are in the optimization-based generation of smooth, collision-free trajectories using B-splines.Real-world experiments were conducted for the surface vehicles and tested the advantages of the proposed enhancements to KDF. The second part of the thesis is devoted to the rendezvous problem of the autonomous landing of a quadrotor on a boat based on distributed Model Predictive Control (MPC) algorithms. We propose an algorithm that assumes a minimal exchange of information between the agents, which is the rendezvous location, and an update rule to maintain the recursive feasibility of the landing. Moreover, a convergence proof is presented without enforcing the terminal set constraints.  Finally, we investigate a leader-follower framework and present an algorithm for multiple follower agents to land autonomously on the landing platform attached to the leader. An agent is equipped with a trajectory predictor to handle the cases of communication loss and avoid inter-agent collisions. The algorithm is tested in a simulation scenario with the simultaneous landing of multiple agents. %and a real-world scenario of the detect-and-avoid problem with two UAVs.

In the third part of the thesis, we examine the usage of the disturbance models and methods to refine them using available data and sensory measurements iteratively.Contraction-based control methods enable safety guarantees for unmanned aerial vehicles with respect to the disturbance-aware plans generated by MPC augmented with disturbance models. Disturbance models are inferred by data-driven identification and learning and further refined using adaptive control methods.The exploration-exploitation algorithm is presented for learning previously unseen disturbances.Finally, the framework is tested in a simulation scenario of the autonomous landing of a UAV on a surface vehicle.

In this thesis, we study trajectory tracking and prediction-based control of underactuated unmanned aerial and surface vehicles.  In the first part of the thesis, we examine the trajectory tracking using prescribed performance control (PPC) assuming that the model parameters are unknown. Moreover, due to the underactuation the original PPC is redesigned to accommodate for the specifics of the considered underactuated systems. We prove the stability of the proposed control schemes and support it with numerical simulations on the quadrotor and boat models. Furthermore, we propose enhancements to kinodynamic motion-planning via funnel control (KDF) framework that are based on rapidly-exploring random tree (RRT) algorithm and B-splines to generate the smooth trajectories and track them with PPC. We conducted real-world experiments and tested the advantages of the proposed enhancements to KDF. The second part of the thesis is devoted to the rendezvous problem of autonomous landing of a quadrotor on a boat based on distributed model predictive control (MPC) algorithms. We propose an algorithm that assumes minimal exchange of information between the agents, which is the rendezvous location, and an update rule to maintain the recursive feasibility of the landing. Moreover, we present a convergence proof without enforcing the terminal set constraints.  Finally, we investigated a leader-follower framework and presented an algorithm for multiple follower agents to land autonomously on the landing platform attached to the leader. An agent is equipped with a trajectory predictor to handle the cases of communication loss and avoid the inter-agent collisions. The algorithm is tested in a simulation scenario with the described challenges and the numerical results support the theoretical findings.

This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous agents, with nonlinear dynamics, are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on distributed Model Predictive Control (MPC). The main contribution is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm only requires the agents to exchange information when they can not guarantee to rendezvous. Hence, the exchange of information occurs aperiodically, which reduces the necessary communication between the agents. Furthermore, we prove that the algorithm guarantees recursive feasibility. The simulation results illustrate the effectiveness of the proposed algorithm applied to the problem of autonomous cooperative landing.

We develop an algorithm to control an underactuated unmanned surface vehicle (USV) using kinodynamic motion planning with funnel control (KDF). KDF has two key components: motion planning used to generate trajectories with respect to kinodynamic constraints, and funnel control, also referred to as prescribed performance control (PPC), which enables trajectory tracking in the presence of uncertain dynamics and disturbances. We extend PPC to address the challenges posed by underactuation and control input saturation present on the USV. The proposed scheme guarantees stability under user-defined prescribed performance functions where model parameters and exogenous disturbances are unknown. Furthermore, we present an optimization problem to obtain smooth, collision-free trajectories while respecting kinodynamic constraints. We deploy the algorithm on a USV and verify its efficiency in real-world open-water experiments.

We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadro-tor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of underactuated systems for which the original PPC methodology cannot be directly applied. We introduce the necessary design modifications to stabilize the considered system with prescribed performance. The proposed control protocol does not use any information of dynamic model parameters or exogenous disturbances. Furthermore, the stability analysis guarantees that the tracking errors remain inside of designer-specified time-varying functions, achieving prescribed performance independent from the control gains’ selection. Finally, simulation results verify the theoretical results.