Terminal Constraint Model Predictive Control for Image-Based Visual Servoing of UAVs with Kalman Filter-Based Moment Loss Compensation

TL;DR

This paper introduces a TC-MPC framework with Kalman filter-based moment loss compensation for UAV image-based visual servoing, enhancing stability and robustness during visual feature loss.

Contribution

It presents a novel control scheme combining terminal-constraint MPC with Kalman filter prediction to improve UAV visual servoing under constraints and feature loss.

Findings

Ensures recursive feasibility and stability under constraints.

Maintains control continuity during visual feature loss.

Validated through real-time UAV experiments.

Abstract

Image-Based Visual Servoing (IBVS) provides an efficient vision-guided control paradigm for unmanned aerial vehicles (UAVs) by directly regulating image-space errors. However, conventional IBVS controllers are vulnerable to two critical issues: loss of closed-loop stability near the target due to input and state constraints, and control failure caused by intermittent loss of moment-based visual features under aggressive motion. To address these challenges, this paper proposes a terminal-constraint model predictive control (TC-MPC) framework for IBVS, integrated with a Kalman filter (KF)-based state-prediction mechanism. The TC-MPC explicitly incorporates terminal-state constraints and a terminal cost into the IBVS error dynamics, ensuring recursive feasibility, improved convergence behavior, and closed-loop stability under control and state constraints. In parallel, the Kalman filter predicts the temporal evolution of image moments during short-term visual degradation, enabling the controller to preserve control continuity when moment measurements are partially unavailable. The proposed approach is validated through real-time UAV visual servoing experiments.