Trajectory Tracking and Prediction-Based Coordination of Underactuated Unmanned Vehicles

QC 20230412

Abstract

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.

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