Pitch Plane Trajectory Tracking Control for Sounding Rockets via Adaptive Feedback Linearization
Pedro dos Santos, Paulo Oliveira

TL;DR
This paper introduces an adaptive feedback linearization control method for pitch plane trajectory tracking of sounding rockets, effectively handling uncertainties and disturbances with minimal re-tuning across different missions.
Contribution
The paper presents a novel adaptive control strategy combining feedback linearization, Lyapunov stability, and LQR for robust trajectory tracking of sounding rockets.
Findings
Successfully tracks trajectories in simulations with external disturbances.
Demonstrates minimal need for re-tuning across different mission scenarios.
Ensures asymptotic stability of the control system.
Abstract
This paper proposes a pitch plane trajectory tacking control solution for suborbital launch vehicles relying on adaptive feedback linearization. Initially, the 2D dynamics and kinematics for a single-engine, thrust-vector-controlled sounding rocket are obtained for control design purposes. Then, an inner-outer control strategy, which simultaneously tackles attitude and position control, is adopted, with the inner-loop comprising the altitude and pitch control and the outer-loop addressing the horizontal (downrange) position control. Feedback linearization is used to cancel out the non-linearities in both the inner and outer dynamics. Making use of Lyapunov stability theory, an adaptation law, which provides online estimates on the inner-loop aerodynamic uncertainty, is jointly designed with the output tracking controller via adaptive backstepping, ensuring global reference tracking in…
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
TopicsAerospace Engineering and Control Systems · Adaptive Control of Nonlinear Systems · Guidance and Control Systems
MethodsRandom Convolutional Kernel Transform
