Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces

Rishabh Dev Yadav, Swati Dantu, Wei Pan, Sihao Sun, Spandan Roy, Simone Baldi

Abstract

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches.

Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces

Abstract

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches.

Paper Structure

This paper contains 17 sections, 1 theorem, 53 equations, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Related Works and Motivation
  3. Contributions of This Study
  4. System Dynamics and Problem Formulation
  5. Proposed Modular Adaptive Control Design and Analysis
  6. Quadrotor Position Control
  7. Quadrotor Attitude Control
  8. Manipulator Control
  9. Overall Control Structure and Stability Analysis
  10. Experimental Results and Analysis
  11. Experimental Scenario
  12. Results and Analysis
  13. Conclusion
  14. Analysis of $\dot{V}_p$
  15. Analysis of $\dot{V}_q$
  16. ...and 2 more sections

Key Result

Theorem 1

Under the Properties prop_1-prop_2, the closed-loop trajectories of (pos)-(man) employing the control laws (ct1), (ct2) and (ct3) along with their respective adaptive laws (adaptive_law_p), (adaptive_law_q) and (adaptive_law_alpha) are Uniformly Ultimately Bounded (UUB).

Figures (7)

  • Figure 1: Schematic for a quadrotor-based UAM system with an $n$-link manipulator and the corresponding frames.
  • Figure 2: Sequence of operations of the quadrotor during the experiment with the proposed controller: (a) takeoff from the ground; (b) follows the trajectory and expand arm to pick the payload; (c) picking the payload and stabilizing itself; (d) moving to the drop location while orienting the arm to the opposite direction; (e) dropping the payload and stabilizing itself (f) reaches to origin. The quadrotor is tied to the floor using a rope for safety reasons.
  • Figure 3: Comparison of quadrotor position tracking error with various controllers.
  • Figure 4: Comparison of quadrotor attitude tracking error with various controllers.
  • Figure 5: Comparison of manipulator tracking error with various controllers.
  • ...and 2 more figures

Theorems & Definitions (5)

  • Remark 1: Uncertainty
  • Remark 2: Unknown inertial couplings
  • Remark 3: Choice of gains and trade-off
  • Theorem 1
  • Remark 4: Role of $\zeta_j$