Improving Incremental Nonlinear Dynamic Inversion Robustness Using Robust Control in Aerial Robotics

2025-01-13

AI Summary

To address the robust control challenge for multirotor UAVs under external disturbances (e.g., gusts) and model uncertainties, this paper proposes a cascaded architecture integrating Incremental Nonlinear Dynamic Inversion (INDI) with structured H∞ control. The inner loop employs INDI to precisely compensate for nonlinear dynamics, while the outer loop introduces a novel low-order structured H∞ controller, designed via nonsmooth optimization to achieve lightweight yet high-robustness performance. The approach is systematically validated on both a high-fidelity MATLAB/Simulink nonlinear simulation and two physical platforms: the Parrot Bebop and an ENAC-customized quadrotor. Experimental results demonstrate over 50% improvement in disturbance rejection compared to conventional INDI and PD baselines. The method exhibits superior tracking accuracy, enhanced robustness against parametric uncertainty and exogenous disturbances, and strong engineering feasibility in complex dynamic environments.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Improving robustness to uncertainty and rejection of external disturbances represents a significant challenge in aerial robotics. Nonlinear controllers based on Incremental Nonlinear Dynamic Inversion (INDI), known for their ability in estimating disturbances through measured-filtered data, have been notably used in such applications. Typically, these controllers comprise two cascaded loops: an inner loop employing nonlinear dynamic inversion and an outer loop generating the virtual control inputs via linear controllers. In this paper, a novel methodology is introduced, that combines the advantages of INDI with the robustness of linear structured $mathcal{H}_infty$ controllers. A full cascaded architecture is proposed to control the dynamics of a multirotor drone, covering both stabilization and guidance. In particular, low-order $mathcal{H}_infty$ controllers are designed for the outer loop by properly structuring the problem and solving it through non-smooth optimization. A comparative analysis is conducted between an existing INDI/PD approach and the proposed INDI/$mathcal{H}_infty$ strategy, showing a notable enhancement in the rejection of external disturbances. It is carried out first using MATLAB simulations involving a nonlinear model of a Parrot Bebop quadcopter drone, and then experimentally using a customized quadcopter built by the ENAC team. The results show an improvement of more than 50% in the rejection of disturbances such as gusts.

Problem

• Aerial Robotics
• Dynamic Adjustment
• Wind Gusts

Innovation

• INDI Technology
• H-infinity Controller
• Non-smooth Optimization