The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.

This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep reinforcement learning theory. Three different approaches implementing the Deep Deterministic Policy Gradient algorithm are presented. Each approach emerges as an improved version of the preceding one. The first approach uses only instantaneous information of the path for solving the problem. The second approach includes a structure that allows the agent to anticipate to the curves. The third agent is capable to compute the optimal velocity according to the path's shape.A training framework that combines the tensorflow-python environment with Gazebo-ROS using the RotorS simulator is built. The three agents are tested in RotorS and experimentally with the Asctec Hummingbird quadrotor. Experimental results prove the validity of the agents, which are able to achieve a generalized solution for the path following problem.

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The methodology to design an aircraft longitudinal path-tracking controller is shown in this paper. First a thorough modelling process which involves the estimation of the aerodynamic and propulsion system structure and parameters is carried out. Next the Lyapunov based backstepping methodology is applied to design a non-linear controller for the aircraft longitudinal dynamics. The procedure used ensures convergence of the system to a reference and robustness in presence of modelling errors. The derived controller delivers manipulated inputs for a real plant and takes into account subsystem and model constraints. Finally, the performance of the controlled system tracking a realistic path is demonstrated in front of parameter uncertainty, unmodelled dynamics and adverse initial conditions.

All real-world systems are affected by the saturation phenomenon due to inherent physical limitations of actuators. These limitations should be taken into account in the controller’s design to prevent a possibly severe deterioration of the system’s performance, and may even lead to instability of the closed-loop system. Contrarily to most of the control strategies, which assume that the saturation limits are constant in time, this paper considers the problem of designing a state-feedback controller for a system affected by time-varying saturation limits with the objective to improve the performance. In order to tie variations of the saturation function to changes in the performance of the closed-loop system, the shifting paradigm is used, that is, some parameters scheduled by the time-varying saturations are introduced to schedule the performance criterion, which is considered to be the instantaneous guaranteed decay rate. The design conditions are obtained within the framework of linear parameter varying (LPV) systems using quadratic Lyapunov functions with constant Lyapunov matrices and they consist in a linear matrix inequality (LMI)-based feasibility problem, which can be solved efficiently using available solvers. Simulation results obtained using an illustrative example demonstrate the validity and the main characteristics of the proposed approach.