Quantile-based predictive control improves gimbal pointing accuracy
Quantile-Based Command-Reserve MPC for Current-Command Gimbal Pointing
Robotics
Summary
Pointing a two-axis gimbal accurately is tough because of mechanical problems like friction and lag. The authors designed a way to predict and measure uncertain disturbances using quantiles, which helps adjust commands more reliably. Their controller keeps commands within safe limits while adapting to uncertainties. Tests on actual hardware show better pointing accuracy and reliable command use with their method.
What this means in practice
- •For robotics engineers: Improve the precision of two-axis gimbals in robotic vision or sensor positioning systems by integrating quantile-based disturbance prediction with adaptive command reserves.
- •For aerospace control teams: Enhance spacecraft or drone line-of-sight pointing systems by reducing errors caused by mechanical disturbances through predictive and adaptive control strategies.
Authors
Yaokun Lu, Yifan Li, Enmei Wang, Hao Teng, Pengwei Hu, Jianzhong Qiao
Abstract
This paper studies the constrained line-of-sight pointing control problem of a two-axis current-command gimbal subjected to mismatched mechanical disturbances. Friction, torque ripple, current-response lag, model mismatch, and limited drive commands make accurate pointing more challenging yet interesting. A quantile disturbance predictor is proposed to compensate predictable acceleration residuals and describe their remaining uncertainty, with the aid of learning median and outer quantiles from state, command, and reference histories. A tube-inspired model predictive controller is proposed to reserve command headroom according to prediction uncertainty, with the aid of calibrated radii and stage-dependent input margins. The median improves the nominal prediction, while the radius adapts the command reserve without adding optimization variables. A finite-horizon tube inclusion result is established for a specified linear error model and fixed ancillary policy; its conditions are distinguished from the approximations used in hardware. Native command bounds and a final command limiter keep the sent commands within their prescribed range. On a two-axis embedded platform, four controller configurations are compared in seven repeated trials each. The proposed configuration achieves a two-dimensional pointing root-mean-square error of $3.94 \pm 0.10$ mrad with no command-limit samples in the evaluation windows. Its performance supports the combined use of residual prediction and adaptive command margins in the tested operating condition. Solver times and logged loop overruns are reported separately for the nominal 100 Hz implementation.