🤖 AI Summary
To address the challenge of aerial manipulators failing to satisfy end-effector tracking performance constraints within a prescribed time, this paper proposes a novel time-sequenced control framework integrating prescribed performance control (PPC) with quadratic programming (QP). Methodologically: (1) A user-tunable prescribed performance function is introduced to rigorously bound both the convergence time and steady-state error of tracking; (2) A task-driven reference command allocation mechanism enables coordinated motion optimization between the quadrotor base and the Delta manipulator; (3) A physically constrained QP formulation ensures actuation safety and feasibility. Experimental results demonstrate that the method achieves high-precision end-effector positioning within the specified time horizon, with tracking errors strictly confined within the prescribed performance envelope throughout execution. This significantly enhances the system’s time determinism and robustness against disturbances and model uncertainties.
📝 Abstract
This paper studies the kinematic tracking control problem for aerial manipulators. Existing kinematic tracking control methods, which typically employ proportional-derivative feedback or tracking-error-based feedback strategies, may fail to achieve tracking objectives within specified time constraints. To address this limitation, we propose a novel control framework comprising two key components: end-effector tracking control based on a user-defined preset trajectory and quadratic programming-based reference allocation. Compared with state-of-the-art approaches, the proposed method has several attractive features. First, it ensures that the end-effector reaches the desired position within a preset time while keeping the tracking error within a performance envelope that reflects task requirements. Second, quadratic programming is employed to allocate the references of the quadcopter base and the Delta arm, while considering the physical constraints of the aerial manipulator, thus preventing solutions that may violate physical limitations. The proposed approach is validated through three experiments. Experimental results demonstrate the effectiveness of the proposed algorithm and its capability to guarantee that the target position is reached within the preset time.