🤖 AI Summary
This work proposes a constraint-aware quadratic programming (QP) control framework for end-effector trajectory tracking of underactuated aerial manipulators under safety and feasibility constraints. The approach explicitly incorporates underactuated dynamics, actuator saturation, and system constraints to directly compute physically feasible generalized accelerations. It innovatively integrates passivity-based integral action at the torque level to enhance robustness against modeling errors and external disturbances. High-fidelity simulations demonstrate that the method achieves high-precision trajectory tracking, smooth control inputs, and reliable constraint satisfaction even in the presence of parameter perturbations, joint friction, and realistic sensing conditions.
📝 Abstract
This paper presents a constraint-aware control framework for underactuated aerial manipulators, enabling accurate end-effector trajectory tracking while explicitly accounting for safety and feasibility constraints. The control problem is formulated as a quadratic program that computes dynamically consistent generalized accelerations subject to underactuation, actuator bounds, and system constraints. To enhance robustness against disturbances, modeling uncertainties, and steady-state errors, a passivity-based integral action is incorporated at the torque level without compromising feasibility. The effectiveness of the proposed approach is demonstrated through high-fidelity physics-based simulations, which include parameter perturbations, viscous joint friction, and realistic sensing and state-estimation effects. This demonstrates accurate tracking, smooth control inputs, and reliable constraint satisfaction under realistic operating conditions.