Nonlinear model predictive controller robustness extension for unmanned aircraft
International Journal of Intelligent Unmanned Systems
ISSN: 2049-6427
Article publication date: 11 May 2015
Abstract
Purpose
Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices.
Design/methodology/approach
In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller.
Findings
The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system.
Research limitations/implications
The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems.
Originality/value
Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.
Keywords
Acknowledgements
This work was completed with funding from the Paul G. Allen Family Foundation (PGAFF) Grant No. FND0071423 and the National Science Foundation (NSF) Center for Remote Sensing of Ice Sheet (CReSIS) Grant No. ANT-0424589. The authors would like to thank PGAFF and CReSIS for their support.
Citation
Garcia, G., Keshmiri, S. and Stastny, T. (2015), "Nonlinear model predictive controller robustness extension for unmanned aircraft", International Journal of Intelligent Unmanned Systems, Vol. 3 No. 2/3, pp. 93-121. https://doi.org/10.1108/IJIUS-01-2015-0002
Publisher
:Emerald Group Publishing Limited
Copyright © 2015, Emerald Group Publishing Limited