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

2025-08-19

AI Summary

To address the poor real-time performance and high energy consumption of conventional tracking control methods under computational resource constraints in aerial systems, this paper proposes a lightweight tracking control framework based on the Newton–Raphson Flow (NRF) method. This work is the first to deploy NRF—endowed with rigorous theoretical convergence guarantees—in practical trajectory tracking tasks for complex aerial platforms, including micro-airships and medium-sized quadcopters, achieving substantial computational savings without compromising tracking accuracy. Experimental results demonstrate that the proposed method attains trajectory tracking errors comparable to or better than those of feedback linearization and nonlinear model predictive control (NMPC), while reducing average computational latency by 42%–68% and CPU energy consumption by 35%–59%. This study establishes a novel lightweight control paradigm for resource-constrained UAVs, uniquely bridging theoretical rigor with practical engineering feasibility.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 has been shown to enjoy theoretical guarantees of performance and has been applied with success in simulation studies and on mobile robots with simple motion models. This paper investigates 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 the established control frameworks of feedback linearization for the blimp, and nonlinear model predictive control for both quadrotor and 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 flow-based tracking controller achieves comparable or superior tracking performance to the baseline methods with substantially reduced computation time and energy expenditure.

Problem

• Lightweight tracking control for computationally constrained aerial systems
• Performance evaluation of Newton-Raphson method in real flight experiments
• Comparison with baseline controllers for tracking error and energy efficiency

Innovation

• Newton-Raphson flow-based tracking controller
• Real-time performance with reduced computation
• Energy-efficient aerial system control