Tightly-Coupled Estimation and Guidance for Robust Low-Thrust Rendezvous via Adaptive Homotopy
New technique reduces terminal miss from 100s of meters to under 1 meter.
A new arXiv paper by Batu Candan and Simone Servadio tackles the brittleness of minimum-fuel low-thrust rendezvous guidance for uncooperative proximity operations. Traditional bang-bang control is highly sensitive to estimation errors and sensor anomalies, leading to large terminal miss distances. The authors propose a tightly-coupled estimation and guidance framework where navigation confidence directly modulates the homotopy parameter of a receding-horizon indirect optimal control solver.
Relative motion is modeled in the Clohessy-Wiltshire frame, with the translational state estimated via a linear Kalman filter augmented by a Multiple Tuning Factors (MTF) covariance inflation mechanism. A composite score from normalized innovation and MTF activity is mapped online to the homotopy parameter, allowing the controller to relax into a smoother, conservative regime when confidence degrades and recover fuel-efficient bang-bang control as sensing improves. Numerical results under severe measurement degradation show the MTF-adaptive homotopy controller reduces terminal miss by roughly two orders of magnitude—from hundreds of meters to sub-meter levels—with only a moderate increase in control effort. The receding-horizon implementation consistently delivers fast and reliable solution times, supporting practical online viability.
- Adaptive homotopy reduces terminal miss distance from ~200 meters to <1 meter in simulated severe sensor degradation tests.
- Multiple Tuning Factors (MTF) Kalman filter suppresses suspicious innovation directions for robust state estimation.
- Receding-horizon solver runs fast enough for real-time online deployment, enabling autonomous uncooperative rendezvous.
Why It Matters
Enables safer autonomous spacecraft rendezvous for debris removal, satellite servicing, and deep-space missions under sensor faults.