Flight Test Results From The Nf 15b Intelligent Flight Control System Ifcs Project With Adaptation To A Simulated Stabilator Failure

Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented. Bosworth, John T. and Williams-Hayes, Peggy S. Armstrong Flight Research Center NASA/TM-2007-214629, H-2751, AIAA-2007-2818 ADAPTIVE CONTROL; FLIGHT CONTROL; NEURAL NETS; FORMATION FLYING; AIRCRAFT STABILITY; STABILITY TESTS; ADAPTATION; ROLL; SYSTEM FAILURES; FLIGHT SIMULATION; STABILIZERS; FLIGHT TEST VEHICLES

Two-volume collection of case studies on aspects of NACA-NASA research by noted engineers, airmen, historians, museum curators, journalists, and independent scholars. Explores various aspects of how NACA-NASA research took aeronautics from the subsonic to the hypersonic era.-publisher description.

This textbook provides readers with a good working knowledge of adaptive control theory through applications. It is intended for students beginning masters or doctoral courses, and control practitioners wishing to get up to speed in the subject expeditiously. Readers are taught a wide variety of adaptive control techniques starting with simple methods and extending step-by-step to more complex ones. Stability proofs are provided for all adaptive control techniques without obfuscating reader understanding with excessive mathematics. The book begins with standard model-reference adaptive control (MRAC) for first-order, second-order, and multi-input, multi-output systems. Treatment of least-squares parameter estimation and its extension to MRAC follow, helping readers to gain a different perspective on MRAC. Function approximation with orthogonal polynomials and neural networks, and MRAC using neural networks are also covered. Robustness issues connected with MRAC are discussed, helping the student to appreciate potential pitfalls of the technique. This appreciation is encouraged by drawing parallels between various aspects of robustness and linear time-invariant systems wherever relevant. Following on from the robustness problems is material covering robust adaptive control including standard methods and detailed exposition of recent advances, in particular, the author’s work on optimal control modification. Interesting properties of the new method are illustrated in the design of adaptive systems to meet stability margins. This method has been successfully flight-tested on research aircraft, one of various flight-control applications detailed towards the end of the book along with a hybrid adaptive flight control architecture that combines direct MRAC with least-squares indirect adaptive control. In addition to the applications, understanding is encouraged by the use of end-of-chapter exercises and associated MATLAB® files. Readers will need no more than the standard mathematics for basic control theory such as differential equations and matrix algebra; the book covers the foundations of MRAC and the necessary mathematical preliminaries.

"Applications of Neural Networks in High Assurance Systems" is the first book directly addressing a key part of neural network technology: methods used to pass the tough verification and validation (V&V) standards required in many safety-critical applications. The book presents what kinds of evaluation methods have been developed across many sectors, and how to pass the tests. A new adaptive structure of V&V is developed in this book, different from the simple six sigma methods usually used for large-scale systems and different from the theorem-based approach used for simplified component subsystems.

This two-volume set (LNAI 8019 and LNAI 8020) constitutes the refereed proceedings of the 10th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2013, held as part of the 15th International Conference on Human-Computer Interaction, HCII 2013, held in Las Vegas, USA in July 2013, jointly with 12 other thematically similar conferences. The total of 1666 papers and 303 posters presented at the HCII 2013 conferences was carefully reviewed and selected from 5210 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The total of 81 contributions included in the EPCE proceedings were carefully reviewed and selected for inclusion in this two-volume set. The papers included in this volume are organized in the following topical sections: driving and transportation safety, cognitive issues in aviation, military applications, cognitive issues in health and well-being.

"Self-Repairing Flight Control System (SRFCS) technology is an extension of traditional redundacy management. It uses existing redundancy in aircraft control surfaces to compensate for control surface failures and battle damage effects. This report describes the results of flight test of the SRFCSc coded in Ada for an Unmanned Research Vehicle (URV). This includes a description of the design of the control reconfiguration method, the Failure Detection (FDU) method, the Ada code, the URV model, data analysis of the flight test time histories, and the computational aspects of the algorithms. Time history plots of selected flight test results are included in this report and acceptable performance was achieved for all failure cases which were flown."--Rept. doc. p.