Development, Implementation, and Experimental Outdoor Evaluation of Quadcopter Controllers for Computationally Limited Embedded Systems
Juan Paredes, Prashin Sharma, Brian Ha, Manuel Lanchares, Ella Atkins,, Peter Gaskell, Ilya Kolmanovsky

TL;DR
This paper presents a comprehensive tutorial on designing, implementing, and experimentally testing quadcopter controllers suitable for embedded systems with limited computational resources, including outdoor flight evaluations.
Contribution
It introduces practical methods for deploying advanced quadcopter controllers on resource-constrained hardware and evaluates their outdoor performance.
Findings
Controllers successfully tracked circular paths outdoors
Performance metrics showed acceptable tracking errors
Computational efficiency was achieved on limited hardware
Abstract
Quadcopters are increasingly used for applications ranging from hobby to industrial products and services. This paper serves as a tutorial on the design, simulation, implementation, and experimental outdoor testing of digital quadcopter flight controllers, including Explicit Model Predictive Control, Linear Quadratic Regulator, and Proportional Integral Derivative. A quadcopter was flown in an outdoor testing facility and made to track an inclined, circular path at different tangential velocities under ambient wind conditions. Controller performance was evaluated via multiple metrics, such as position tracking error, velocity tracking error, and onboard computation time. Challenges related to the use of computationally limited embedded hardware and flight in an outdoor environment are addressed with proposed solutions.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Control Systems Optimization · Real-time simulation and control systems · Adaptive Control of Nonlinear Systems
