Engine Yaw Augmentation for Hybrid-Wing-Body Aircraft via Optimal Control Allocation Techniques

Asymmetric engine thrust was implemented in a hybrid-wing-body non-linear simulation to reduce the amount of aerodynamic surface deflection required for yaw stability and control. Hybrid-wing-body aircraft are especially susceptible to yaw surface deflection due to their decreased bare airframe yaw stability resulting from the lack of a large vertical tail aft of the center of gravity. Reduced surface deflection, especially for trim during cruise flight, could reduce the fuel consumption of future aircraft. Designed as an add-on, optimal control allocation techniques were used to create a control law that tracks total thrust and yaw moment commands with an emphasis on not degrading the baseline system. Implementation of engine yaw augmentation is shown and feasibility is demonstrated in simulation with a potential drag reduction of 2 to 4 percent. Future flight tests are planned to demonstrate feasibility in a flight environment

Обучение нейроэмуляторов с использованием псевдорегуляризации для метода нейроуправления с эталонной моделью

В статье рассматриваются задачи идентификации и управления для нелинейного динамического объекта на основе использования нейронных сетей. Излагается метод применения расширенного фильтра Калмана для обучения нейронных сетей. Предлагается метод псевдорегуляризации для эффективного обучения нейроэмуляторов в методе нейроуправления с эталонной моделью. Приводятся результаты множественных экспериментов по обучению нейроэмуляторов и нейроконтроллеров.У статті розглядаються задачі ідентифікації та управління для нелінійного динамічного об’єкта на основі використання нейронних мереж. Викладається метод застосування розширеного фільтра Калмана для навчання нейронних мереж. Пропонується метод псевдорегулярізаціі для ефективного навчання нейро-емулятора в методі нейроуправління з еталонною моделлю. Наводяться результати експериментів з навчання нейроемуляторів і нейроконтролерів.The problems of identification and control for nonlinear dynamic object with use of neural networks are considered. The Extended Kalman Filter method for neural networks training is described. Pseudoregularization method for effective training of neuroemulator for Model Reference Adaptive Neurocontrol is proposed. The results of numerical experiments for training of neuroemulators and neurocontrollers are presented

Validation and Verification of Aircraft Control Software for Control Improvement

Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system

Обзор методов нейроуправления

Рассматриваются методы применения нейронных сетей для решения задач управления динамическими объектами. Для каждого вида нейроуправления приводятся схемы соединения нейросетей внутри системы управления и детально описываются процедуры их обучения. Анализируются преимущества и недостатки описанных методов

Нейромережеве управління електромеханічною системою з пружними зв’язками в кінематичних передачах

Розроблена математична модель двомасової системи управління електроприводом генератор-двигун, яка має структуру з підсумовуючим підсилювачем. Шляхом моделювання системи встановлено, що в перехідних режимах мають місце значні коливання основних координат системи. Для забезпечення бажаних динамічних характеристик двомасової системи обґрунтовано застосування нейромережевих технологій управління. Розроблена структурна схема нейромережевої системи. В якості нейрорегулятора вибрано регулятор з передбаченням NN Predictive Controller, що міститься в пакеті прикладних програм Neural Network Toolbox системи MATLAB. Проведено моделювання нейромережевої системи. В результаті аналізу результатів моделювання встановлено, що нейромережева система забезпечує високу якість регулювання

Emergency Flight Planning for a Generalized Transport Aircraft with Left Wing Damage

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77332/1/AIAA-2007-6873-998.pd

Augmentation of an Intelligent Flight Control System for a Simulated C-17 Aircraft

This paper examines a neural-adaptive flight control system augmented with linear programming theory and adaptive critic techniques for a simulated C-17 aircraft. The baseline Intelligent Flight Control (IFC) system is composed of a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and make the controller easy to apply when implemented on different aircraft. In this study, IFC has been augmented with linear programming (LP) theory and adaptive critic technologies. LP is used to optimally allocate requested control deflections and the adaptive critic modifies the parameters of the aircraft reference model for consistent handling qualities. Full-motion piloted simulation studies were performed on a Boeing C-17. Subjects included NASA and Air Force pilots. Results, including subjective pilot ratings and time response characteristics of the system, demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions. B = control derivative matrix e = error f = generic function J = cost function k = reference model gain M = pitching moment p = roll rate q = pitch rate r = yaw rate X = state vector t = time U = utility function u = control vector w = weighting vector δa = aileron deflection δe γ elevator deflection discount facto

Emergency Flight Planning for the Generalized Transport Model Aircraft with Left Wing Damage

A nontrivial fraction of aviation accidents are caused by in-flight damage or failures that reduce performance. Researchers are working to ensure future avionics recognize the impact of damage/failures and guide the aircraft to a safe landing. This thesis presents an end-to-end Adaptive Flight Planner (AFP) for such emergencies and applies it to a damage situation in which a Generalized Transport Model (GTM) aircraft loses a significant fraction of its left wingtip. Trimmed (non-accelerating) flight conditions define the post-damage/failure aircraft flight envelope. A landing site search algorithm is augmented to define the reachable landing footprint and to prioritize the feasible landing runways within this region. End-to-end landing trajectories are constructed as a sequence of trim states and corresponding transitions. An LQR-based PID nonlinear controller enables the damaged GTM aircraft to correctly track trajectory commands over trimmed flight and transition segments. A suite of emergency scenarios are used to evaluate AFP performance