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

2025-04-08

AI Summary

To address the challenge of stable trajectory tracking in dual-arm aerial manipulator systems—caused by time-varying center-of-mass dynamics, modeling inaccuracies, parametric uncertainties, and external disturbances—this paper proposes a deep neural network (DNN)-enhanced adaptive Robust Integral of the Sign of the Error (RISE) controller. The method integrates rigid-body dynamic modeling with DNN-based feedforward compensation, pioneering the incorporation of deep learning into the RISE framework for online estimation of composite uncertainties. Rigorous Lyapunov analysis guarantees global asymptotic stability of the closed-loop system. Extensive experiments on a physical hardware platform demonstrate a 42.6% reduction in trajectory tracking error and a 58% decrease in disturbance rejection response time, enabling high-precision, cooperative dual-arm manipulation under complex dynamic conditions. This work establishes a novel paradigm for robust adaptive control of unmanned systems with time-varying center-of-mass characteristics.

Technology Category

• Intelligent Robots: Manipulation
• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Dynamic challenge from dual-arm manipulator CoM changes
• Unmodeled effects and disturbances degrading control performance
• Need robust control for aerial manipulator stability

Innovation

• DNN-based adaptive RISE controller design
• Lyapunov techniques ensure error convergence
• Hardware experiments validate robustness