Lightweight Tracking Control for Computationally Constrained Aerial Systems with the Newton-Raphson Method

Abstract

We investigate the performance of a lightweight tracking controller, based on a flow version of the Newton-Raphson method, applied to a miniature blimp and a mid-size quadrotor. This tracking technique admits theoretical performance guarantees for certain classes of systems and has been successfully applied in simulation studies and on mobile robots with simplified motion models. We evaluate the technique through real-world flight experiments on aerial hardware platforms subject to realistic deployment and onboard computational constraints. The technique's performance is assessed in comparison with established baseline control frameworks of feedback linearization for the blimp, and nonlinear model predictive control for both the quadrotor and the blimp. The performance metrics under consideration are (i) root mean square error of flight trajectories with respect to target trajectories, (ii) algorithms' computation times, and (iii) CPU energy consumption associated with the control algorithms. The experimental findings show that the Newton-Raphson-based tracking controller achieves competitive or superior tracking performance to the baseline methods with substantially reduced computation time and energy expenditure.

Figures (9)

• Figure 1: Helix trajectory tracking with the Newton-Raphson-based tracking technique.
• Figure 2: Basic feedback control system.
• Figure 3: Empirical sensitivity of tracking error to the controller-speedup factor $\alpha$ associated with the thrust input $u_{\tau}$.
• Figure 4: The radially symmetric blimp with relevant coordinate frames. The gondola includes six motors---two for vertical actuation, and four for horizontal actuation and yawing.
• Figure 5: Blimp: Comparison of five standard flight trajectories. Flight data in red, trajectory reference in blue. Rows from top to bottom are NR-based technique (labeled by the acronym NRT), NMPC, and FBL-based controller.