Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

Abstract

Inspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control theory that would guarantee energy consumption close to optimal with lower computational cost. First, a least-squares-estimation-(LSE) algorithm identified the parameters of the UAV mathematical model. Then, by considering a precise electrical model for the brushless DC motors and rest-to-rest maneuvers, the extraction of clear rules to compute the optimal mission time and generate ’energetic trajectories’ was performed. These rules emerged from analyzing the optimal-control strategy results that minimized the consumption over many simulations. Afterward, a hierarchical controller tracked those desired energetic trajectories identified as sub-optimal. Numerical experiments compared the results regarding trajectory tracking, energy performance index, and battery state of charge (SOC). A co-simulation framework consisting of commercial software tools, Simcenter Amesim for the physical modeling of the UAV, and Matlab-Simulink executed numerical simulations of the implemented controller.

Optimization-Based Control for a Large-Scale Electrical Vertical Take-Off and Landing during an Aircraft’s Vertical Take-Off and Landing Phase with Variable-Pitch Propellers

“…Different from the four control inputs, which are almost related to all eight control outputs, the power function of each propeller is dependent on only its rotation speed and pitch angle, as described in Equation (14). Therefore, the partial derivatives of power vector P with respect to the rotation speed vector ω and pitch angle vector α are both diagonal matrices, which can be written as…”

“…Portillo et al [13] discussed a method to eliminate unmodeled dynamics or external disturbances at the control level, which provides a feasible solution to overcome these problems. Meanwhile, Bianchi et al [14] addressed the energy consumption of quadrotors when tracking a trajectory in an analytical way. Though the proposed energy consumption function cannot be directly utilized in our work, the calculating ideas of the authors are still worth learning from.…”

Correction: Bianchi et al. Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator. Drones 2024, 8, 29

“…In the original publication [1], Figure 1 was removed since it is the same as Figure 1 in [27]. This was a mistake; this figure was included informally by us in a preliminary private draft and accidentally left in the submission.…”