Lightweight Tracking Control for Computationally Constrained Aerial Systems with the Newton-Raphson Method
This addresses the challenge of efficient control for computationally constrained aerial systems, such as miniature blimps and quadrotors, with incremental improvements in performance and resource usage.
The paper tackled the problem of developing a lightweight tracking controller for aerial systems under computational constraints, using a Newton-Raphson-based method, and found it achieved competitive or superior tracking performance with significantly reduced computation time and energy expenditure compared to baseline methods.
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.