Robust and Adaptive Nonlinear Model Predictive Controller for Unsteady and Highly Nonlinear Unmanned Aircraft

Abstract

The nonlinear and unsteady nature of aircraft aerodynamics in the presence of adverse conditions and external disturbances, together with a limited range of flight variables makes the use of the linear control theory inadequate in such conditions. To address these constraints and significantly enhance aircraft control capabilities, this brief presents an adaptive framework for a robust nonlinear model predictive control (NMPC). Control algorithms are tested on a 1100-pound unmanned aerial system, with nonlinear, coupled, and unstable open-loop dynamics, subjected to environmental disturbances and measurement noise. Given the usual frequency content exclusion between disturbance and noise, this solution addresses the lack of robustness in model predictive control by the inclusion of frequency-dependent weighting matrices and a nonlinear version of the mixed sensitivity approach. Furthermore, real-time aerodynamic parameter estimation and predictive model updating is carried out by online adaptive artificial neural networks. Through assessment and validity of control algorithms, it is demonstrated that two originally competing control concepts, robustness and performance, are integrally attained in real time. This is usually unreachable in the classical NMPC framework for complex systems.