Recursive real-time identification of step-response matrices of high-performance aircraft for adaptive digital flight control

Author(s):  
B. PORTER ◽  
A. MANGANAS
2018 ◽  
Vol 25 (5) ◽  
pp. 1044-1057 ◽  
Author(s):  
Hongkun Li ◽  
Rui Huang ◽  
Yonghui Zhao ◽  
Haiyan Hu

The design of a robust maneuver load alleviation (MLA) system for a high-performance aircraft is studied in this paper. First, the aeroservoelastic (ASE) models of a high-performance military aircraft in climbing maneuver at varying Mach numbers are established. Then, a linear parameter-varying (LPV) model of the ASE systems is constructed and an [Formula: see text] robust controller is designed based on the LPV model. The robust control is realized via a pair of outboard ailerons to alleviate the wing-root bending moments in the climbing maneuvers. To compensate the loss of performance in the load alleviation, a controller based on recurrent neural networks is designed in the flight control. Finally, some numerical simulations are made to testify the performance and robustness of the MLA system.


2020 ◽  
Vol 49 (1) ◽  
pp. 28-35
Author(s):  
Sezer Coban

In this study, it is examined that simultaneous flight control system and lateral and longitudional state-space model of a Unmanned Aerial Vehicle (UAV) and real time application. For this purpose an UAV whose wing and tail unit can be assembled to fuselage from different points in a prescribed interval and whose wing and tail can move forward and backward independently in tail to nose direction is manufactured. Following this, an autopilot is purchased and it lets change of P, I, D coefficients in certain intervals. First, dynamic model, and longitudinal and lateral state space models of UAV are obtained and then simulation model of UAV is reached. At the same time block diagram of autopilot system and modeling of it in MATLAB/Simulink environment are found. After these, using these two models and also benefiting and adaptive stochastic optimization method namely SPSA, simultaneous design of UAV and autopilot is done in order to minimize a cost function consisting of rise time, settling time and maximum overshoot. Therefore, primarily autonomous performance is maximized in computer environment. Moreover, high performance is observed by looking at simulation responses and real-time flights.


2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Adel A. Ghandakly ◽  
Jason A. Reed

This paper presents a development, as well as an investigation of a Model Matching Controller (MMC) design based on the Self-Tuning Regulator (STR) framework for high performance aircraft with direct application to an F-16 aircraft flight control system. In combination with the Recursive Least Squares (RLS) identification, the MMC is developed and investigated for effectiveness on a detailed model of the aircraft. The popular robust Quantitative Feedback Theory (QFT) controller is also outlined and used to represent a baseline controller, for performance comparison during four simulated test flight maneuvers. In each of the four maneuvers, the proposed MMC provided consistently stable and satisfactory performance, including the challenging pull-up and pushover maneuvers. The baseline stationary controller has been found to become unstable in two of the four maneuvers tested. It also performs satisfactorily-to-arguably poorly in the remaining two as compared to the MMC. Simulation results presented in this investigation support a clear argument that the proposed MMC provides superior performance in the realm of automatic flight control.


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