Online Identification using Adaptive Laws and Neural Networks for Multi-Quadrotor Centralized Transportation System

2025-09-02

AI Summary

Centralized multi-quadrotor payload transportation systems suffer from insufficient robustness against time-varying disturbances and model uncertainties. Method: This paper proposes a model-free online identification and feedforward compensation scheme. Contribution/Results: The core innovation lies in decomposing the high-dimensional modeling error space into low-dimensional subspaces, each approximated in parallel by shallow neural networks coupled with adaptive laws—eliminating the need for persistent excitation conditions or offline training. The resulting control law, rigorously designed via Lyapunov stability theory, requires no precise knowledge of payload-coupled dynamics. Simulation results demonstrate substantial improvements in trajectory tracking accuracy and robust stability under severe time-varying disturbances and parametric uncertainties.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper introduces an adaptive-neuro identification method that enhances the robustness of a centralized multi-quadrotor transportation system. This method leverages online tuning and learning on decomposed error subspaces, enabling efficient real-time compensation to time-varying disturbances and model uncertainties acting on the payload. The strategy is to decompose the high-dimensional error space into a set of low-dimensional subspaces. In this way, the identification problem for unseen features is naturally transformed into submappings (``slices'') addressed by multiple adaptive laws and shallow neural networks, which are updated online via Lyapunov-based adaptation without requiring persistent excitation (PE) and offline training. Due to the model-free nature of neural networks, this approach can be well adapted to highly coupled and nonlinear centralized transportation systems. It serves as a feedforward compensator for the payload controller without explicitly relying on the dynamics coupled with the payload, such as cables and quadrotors. The proposed control system has been proven to be stable in the sense of Lyapunov, and its enhanced robustness under time-varying disturbances and model uncertainties was demonstrated by numerical simulations.

Problem

• Enhancing robustness in multi-quadrotor centralized transportation systems
• Compensating time-varying disturbances and model uncertainties online
• Eliminating need for persistent excitation and offline training

Innovation

• Online adaptive-neuro identification method
• Decomposed error subspaces learning
• Lyapunov-based neural network compensation