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PreGME: Prescribed Performance Control of Aerial Manipulators based on Variable-Gain ESO

Mengyu Ji, Shiliang Guo, Zhengzhen Li, Jiahao Shen, Huazi Cao, Shiyu Zhao

TL;DR

This work tackles the challenge of dynamic coupling in aerial manipulators, where the quadrotor base and the robotic arm interact strongly and rapidly. It introduces PreGME, a prescribed-performance control framework that couples a variable-gain extended state observer (ESO) with error-trajectory constraints to achieve high-precision tracking under unknown disturbances. The approach yields two main contributions: (1) a partially decoupled framework using variable-gain ESOs to estimate fast-changing coupling disturbances $\bm{\Delta}_v$ and $\bm{\Delta}_\omega$, and (2) a novel prescribed-performance flight control with smooth, non-singular preset error trajectories that confine the tracking errors within a user-defined envelope. Experimental validation on a real aerial manipulator demonstrates improved position and attitude accuracy under aggressive arm motions, confirming robust disturbance rejection and preservation of prescribed performance across tasks such as staff twirling, aerial mixology, and cart-pulling.

Abstract

An aerial manipulator, comprising a multirotor base and a robotic arm, is subject to significant dynamic coupling between these two components. Therefore, achieving precise and robust motion control is a challenging yet important objective. Here, we propose a novel prescribed performance motion control framework based on variable-gain extended state observers (ESOs), referred to as PreGME. The method includes variable-gain ESOs for real-time estimation of dynamic coupling and a prescribed performance flight control that incorporates error trajectory constraints. Compared with existing methods, the proposed approach exhibits the following two characteristics. First, the adopted variable-gain ESOs can accurately estimate rapidly varying dynamic coupling. This enables the proposed method to handle manipulation tasks that require aggressive motion of the robotic arm. Second, by prescribing the performance, a preset error trajectory is generated to guide the system evolution along this trajectory. This strategy allows the proposed method to ensure the tracking error remains within the prescribed performance envelope, thereby achieving high-precision control. Experiments on a real platform, including aerial staff twirling, aerial mixology, and aerial cart-pulling experiments, are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that even under the dynamic coupling caused by rapid robotic arm motion (end-effector velocity: 1.02 m/s, acceleration: 5.10 m/s$^2$), the proposed method achieves high tracking performance.

PreGME: Prescribed Performance Control of Aerial Manipulators based on Variable-Gain ESO

TL;DR

This work tackles the challenge of dynamic coupling in aerial manipulators, where the quadrotor base and the robotic arm interact strongly and rapidly. It introduces PreGME, a prescribed-performance control framework that couples a variable-gain extended state observer (ESO) with error-trajectory constraints to achieve high-precision tracking under unknown disturbances. The approach yields two main contributions: (1) a partially decoupled framework using variable-gain ESOs to estimate fast-changing coupling disturbances and , and (2) a novel prescribed-performance flight control with smooth, non-singular preset error trajectories that confine the tracking errors within a user-defined envelope. Experimental validation on a real aerial manipulator demonstrates improved position and attitude accuracy under aggressive arm motions, confirming robust disturbance rejection and preservation of prescribed performance across tasks such as staff twirling, aerial mixology, and cart-pulling.

Abstract

An aerial manipulator, comprising a multirotor base and a robotic arm, is subject to significant dynamic coupling between these two components. Therefore, achieving precise and robust motion control is a challenging yet important objective. Here, we propose a novel prescribed performance motion control framework based on variable-gain extended state observers (ESOs), referred to as PreGME. The method includes variable-gain ESOs for real-time estimation of dynamic coupling and a prescribed performance flight control that incorporates error trajectory constraints. Compared with existing methods, the proposed approach exhibits the following two characteristics. First, the adopted variable-gain ESOs can accurately estimate rapidly varying dynamic coupling. This enables the proposed method to handle manipulation tasks that require aggressive motion of the robotic arm. Second, by prescribing the performance, a preset error trajectory is generated to guide the system evolution along this trajectory. This strategy allows the proposed method to ensure the tracking error remains within the prescribed performance envelope, thereby achieving high-precision control. Experiments on a real platform, including aerial staff twirling, aerial mixology, and aerial cart-pulling experiments, are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that even under the dynamic coupling caused by rapid robotic arm motion (end-effector velocity: 1.02 m/s, acceleration: 5.10 m/s), the proposed method achieves high tracking performance.
Paper Structure (18 sections, 2 theorems, 24 equations, 6 figures, 1 table)

This paper contains 18 sections, 2 theorems, 24 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries and Control System Overview
  3. Dynamics of the Aerial Manipulator
  4. Variable-Gain Extended State Observer
  5. Tracking Error Trajectory Constraints
  6. Control System Overview
  7. Prescribed Performance Control based on Variable-Gain ESO
  8. Position ESO
  9. Position Control
  10. Attitude ESO
  11. Attitude Control
  12. Numerical Validation
  13. Experimental Verification
  14. Experimental Setup
  15. Example 1: Aerial Staff Twirling
  16. ...and 3 more sections

Key Result

Theorem 1

Let $\lambda_{\min}(\bm K_p)$ be the minimum eigenvalue of $\bm K_p$, and $[\Lambda_p]_{i,i}$ the $i$-th diagonal element of $\bm \Lambda_p$. Assuming that $\bm \Delta_v$ and its derivative $\dot{\bm \Delta}_v$ are bounded, for the $i$-th element $\tilde{ p}_i(t)$ of the tracking error $\tilde{\bm p

Figures (6)

  • Figure 1: Aerial mixology by an aerial manipulator. The experimental video is available at https://youtu.be/LGKp5rYE8GU.
  • Figure 2: The proposed control framework of the aerial manipulator system.
  • Figure 3: Results of the setpoint tracking experiment. (a) Visual description of the experiment. (b) The trajectories of the quadcopter base. (c) Position and attitude tracking results of the quadcopter base. (d) Tracking results of robotic arm joints.
  • Figure 4: Results of the aerial staff twirling experiment. (a) Visual description of the experiment. The reference trajectory follows a figure-eight curve defined by $x= 0.65\sin (4\pi t/T_p)$ and $y = 1.3\sin(2\pi t/T_p)$, with the trajectory period set to $T_p=16$ s. (b) Top view of the experiment. (c) Snapshots of the staff twirling. (d) Tracking results of the 1st, 4th, and 6th joints of the robotic arm. (e) Position tracking errors of the quadcopter base. The error plots depict the mean (solid line) and standard deviation (shaded region) calculated from ten repeated trials. (f) Raincloud plot of position tracking error distributions for the quadcopter base under three control methods.
  • Figure 5: Results of the aerial mixology experiment. (a) Visual description of the experiment. (b) Pre-shake layered materials and post-shake cocktail. (c) Tracking results of the 1st, 2nd, and 6th robotic arm joints. (d) Quadcopter position tracking errors during shaking. The error plots depict the mean (solid line) and standard deviation (shaded region) calculated from ten repeated trials. (e) Raincloud plot of position tracking error distributions for the quadcopter base under three control methods.
  • ...and 1 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Theorem 2
  • proof