Abstract
In this paper, a suboptimal nonlinear discrete control for nonlinear discrete affine systems is implemented in an autonomous soaring unmanned aerial vehicle (UAV) to improve energy consumption and performance. General expressions for this controller are obtained from the continuous nonlinear guidance model of a fixed-wing UAV and applied in a multiloop control structure. Simulation results in the task of trajectory tracking inside a thermal updraft and a comparative study with a proportional derivative (PD) controller validated the effectiveness of this method.
Highlights
In recent years, researchers have become increasingly interested in unmanned aerial vehicles (UAVs) due to the number of applications they can be used for and the advantages in using them
In [11], adaptive formation controllers for fixed-wing UAVs and a softwarein-the-loop are designed and implemented, these adaptive formation controllers are designed to lead with parametric uncertainties such as mass and inertia, and software-in-theloop simulations show the effectiveness of these controllers handling uncertain mass and inertia in the task of path following with multiple UAVs
Two simulations were performed: the first one using the suboptimal nonlinear discrete control laws developed in this work and the second one using proportional derivative (PD) controllers with their parameters overshot and settling time heuristically tuned
Summary
Researchers have become increasingly interested in unmanned aerial vehicles (UAVs) due to the number of applications they can be used for and the advantages in using them. In [11], adaptive formation controllers for fixed-wing UAVs and a softwarein-the-loop are designed and implemented, these adaptive formation controllers are designed to lead with parametric uncertainties such as mass and inertia, and software-in-theloop simulations show the effectiveness of these controllers handling uncertain mass and inertia in the task of path following with multiple UAVs. In this work, the suboptimal control method proposed minimizes the performance index for nonlinear discrete affine systems using the dynamic programming approach without the need to solve the Bellman equation; for linear systems, the problem has been solved but the backward solution has the problem called “course of dimensionality” where it is necessary to solve a Riccati equation, and the more large is the final horizon (final time), the more the computational power is needed.
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