QP-Based Control of an Underactuated Aerial Manipulator under Constraints

TL;DR

This work tackles constraint-aware control for underactuated aerial manipulators by proposing a centralized two-loop framework that combines a task-space outer controller with a QP-based inner optimizer to compute dynamically consistent generalized accelerations while enforcing actuation and safety constraints. A passivity-based integral action at the torque level enhances disturbance rejection and steady-state accuracy without compromising feasibility. The method explicitly handles underactuation, actuator limits, and joint limits, and uses a high-fidelity Simscape Multibody environment to demonstrate real-time feasibility and robust performance under model uncertainties and sensor noise. The approach yields precise end-effector tracking, smooth control inputs, and reliable constraint satisfaction, offering a practical pathway toward safe, real-time aerial manipulation in complex environments.

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.

Figures (12)

• Figure 1: CAD model of the aerial manipulator consisting of a quadrotor base and a 5--DoF arm.
• Figure 2: Block diagram of the proposed control architecture.
• Figure 3: Simscape Multibody visualization of the aerial manipulator tracking the base position, yaw angle, and end-effector setpoints.
• Figure 4: Floating-base position and yaw angle regulation under set-point stabilization and model uncertainties.
• Figure 5: End-effector position regulation under set-point stabilization and model uncertainties.