Adaptive RISE Control for Dual-Arm Unmanned Aerial Manipulator Systems with Deep Neural Networks

TL;DR

This work tackles the control challenges of dual-arm aerial manipulators whose dynamics are complicated by changing center-of-mass and external disturbances. It proposing a nonlinear adaptive RISE controller augmented with a deep neural network feedforward term, with online weight adaptation and a Lyapunov-based stability proof ensuring asymptotic convergence of tracking errors. Theoretical analysis is complemented by hardware experiments on a self-built platform, showing substantial improvements in position tracking accuracy and robustness across figure-eight and spiral trajectories, as well as successful collaborative grasping and delivery. The results demonstrate the practical viability of combining DNN feedforward with robust RISE feedback for safe and capable aerial manipulation in real-world tasks.

Abstract

The unmanned aerial manipulator system, consisting of a multirotor UAV (unmanned aerial vehicle) and a manipulator, has attracted considerable interest from researchers. Nevertheless, the operation of a dual-arm manipulator poses a dynamic challenge, as the CoM (center of mass) of the system changes with manipulator movement, potentially impacting the multirotor UAV. Additionally, unmodeled effects, parameter uncertainties, and external disturbances can significantly degrade control performance, leading to unforeseen dangers. To tackle these issues, this paper proposes a nonlinear adaptive RISE (robust integral of the sign of the error) controller based on DNN (deep neural network). The first step involves establishing the kinematic and dynamic model of the dual-arm aerial manipulator. Subsequently, the adaptive RISE controller is proposed with a DNN feedforward term to effectively address both internal and external challenges. By employing Lyapunov techniques, the asymptotic convergence of the tracking error signals are guaranteed rigorously. Notably, this paper marks a pioneering effort by presenting the first DNN-based adaptive RISE controller design accompanied by a comprehensive stability analysis. To validate the practicality and robustness of the proposed control approach, several groups of actual hardware experiments are conducted. The results confirm the efficacy of the developed methodology in handling real-world scenarios, thereby offering valuable insights into the performance of the dual-arm aerial manipulator system.

Figures (8)

• Figure 1: Illustration of dual-arm aerial manipulator system.
• Figure 2: Structure of the control system.
• Figure 3: Hardware experimental platform.
• Figure 4: Joint angles of the dual-arm.
• Figure 5: Snaps of the three groups of actual experiments.

Theorems & Definitions (1)

• Theorem 1