Engineering topics
Publications and source records attributed to Bosworth, John.
A viewgraph presentation on the satus of NASA Dryden's aircraft guidance and control is shown. The topics include: 1) Autonomous Airborne Refueling Demonstration (AARD); 2) Ikhana Project Update; 3) Quiet Spike; 4) F-15 Intelligent Flight Control System; 5) C-20A Precision Autopilot Development; and 6) X-48 Blended Wing Body.
This viewgraph presentation describes the F-15 Intelligent Flight Control System (IFCS). The goals of this project include: 1) Demonstrate revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions; and 2) Demonstrate advance neural network-based flight control technology for new aerospace systems designs.
The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.
A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions
Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.
This report describes the organization, activities, meetings held and the current challenges of the Central Bureau of the ILRS.