Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Adaptive Gain Nonlinear Observer for External Wrench Estimation in Human-UAV Physical Interaction

Hussein N. Naser, Hashim A. Hashim, Mojtaba Ahmadi

Abstract

This paper presents an Adaptive Gain Nonlinear Observer (AGNO) for estimating the external interaction wrench (forces and torques) in human-UAV physical interaction for assistive payload transportation. The proposed AGNO uses the full nonlinear dynamic model to achieve an accurate and robust wrench estimation without relying on dedicated force-torque sensors. A key feature of this approach is the explicit consideration of the non-constant inertia matrix, which is essential for aerial systems with asymmetric mass distribution or shifting payloads. A comprehensive dynamic model of a cooperative transportation system composed of two quadrotors and a shared payload is derived, and the stability of the observer is rigorously established using Lyapunov-based analysis. Simulation results validate the effectiveness of the proposed observer in enabling intuitive and safe human-UAV interaction. Comparative evaluations demonstrate that the proposed AGNO outperforms an Extended Kalman Filter (EKF) in terms of estimation root mean square errors (RMSE), particularly for torque estimation under nonlinear interaction conditions. This approach reduces system weight and cost by eliminating additional sensing hardware, enhancing practical feasibility.

Adaptive Gain Nonlinear Observer for External Wrench Estimation in Human-UAV Physical Interaction

Abstract

This paper presents an Adaptive Gain Nonlinear Observer (AGNO) for estimating the external interaction wrench (forces and torques) in human-UAV physical interaction for assistive payload transportation. The proposed AGNO uses the full nonlinear dynamic model to achieve an accurate and robust wrench estimation without relying on dedicated force-torque sensors. A key feature of this approach is the explicit consideration of the non-constant inertia matrix, which is essential for aerial systems with asymmetric mass distribution or shifting payloads. A comprehensive dynamic model of a cooperative transportation system composed of two quadrotors and a shared payload is derived, and the stability of the observer is rigorously established using Lyapunov-based analysis. Simulation results validate the effectiveness of the proposed observer in enabling intuitive and safe human-UAV interaction. Comparative evaluations demonstrate that the proposed AGNO outperforms an Extended Kalman Filter (EKF) in terms of estimation root mean square errors (RMSE), particularly for torque estimation under nonlinear interaction conditions. This approach reduces system weight and cost by eliminating additional sensing hardware, enhancing practical feasibility.
Paper Structure (25 sections, 1 theorem, 39 equations, 5 figures, 1 table)

This paper contains 25 sections, 1 theorem, 39 equations, 5 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Related Work
  3. Contributions
  4. Methodology
  5. System Overview and Problem Formulation
  6. Reference Frames and Notation
  7. The Payload Dynamics
  8. The Quadrotor Dynamics
  9. The Integrated System Dynamics
  10. Control Architecture
  11. The Proposed Nonlinear Observer Design
  12. The Proposed Nonlinear Observer
  13. Acceleration-Free Implementation
  14. Stability, Convergence, and Robustness Analysis
  15. Stability Analysis for Observer Design
  16. ...and 10 more sections

Key Result

Theorem 1

Consider the nonlinear estimator of the external interaction wrench in eq26:auxiliary_wrench_observer_dynamics and let Assumptions assum:4 to assum:7 hold, the design of $\mathbb{B}(\xi,\dot{\xi})$ in eq27:B_matrix ensures asymptotic stability of the error dynamics in eq21:error_dynamics in the sens

Figures (5)

  • Figure 1: Free body diagram of the system components.
  • Figure 2: Control system architecture.
  • Figure 3: The simulation environment; MATLAB simulation of the aerial payload transport system's motion following human guidance in 3D space as depicted by the arrows (magnitudes and directions).
  • Figure 4: The actual and estimated forces in $(x,y,z)$ directions and torque around $z$ axis.
  • Figure 5: Estimation errors of the external interaction forces in $(x,y,z)$ directions and torque around $z$ axis.

Theorems & Definitions (1)

  • Theorem 1