ment the detection of conceptual design errors early in development, when they are much less expensive to correct; decrease development time and risk; and allow for greater reuse of system engineering effort. Research should strive to improve analysis and specification of design rationale, visualization and simulation tools, and so forth. Key milestones include

  • Develop easily used and reviewed modeling languages.

  • Develop automated design and code generation.

Relevance to Strategic Objectives

Capacity (3): The development of new aircraft capabilities is increasingly being driven by the requirement to develop and upgrade software. Software development is a component of most approaches to increasing capacity, but it is not the most critical component.

Safety and Reliability (9): Because the behavior of complex systems is increasingly controlled by software, software can have a significant impact on safety and reliability.

Efficiency and Performance (3): Software costs are a driving factor in design and development. Improving design, development, and upgrade processes would increase efficiency and performance.

Energy and the Environment (1): This Challenge has relatively little effect on energy use and the environment, and the effect is indirect.

Synergies with National and Homeland Security (1): Software-intensive systems might be used to implement some new technologies, but the benefit to DoD and DHS would be indirect.

Support to Space (3): Some of the R&T relevant to this Challenge would apply to space control systems, but design requirements are very different.

Why NASA?

Supporting Infrastructure (1): NASA has some capable researchers in fields related to this Challenge, but NASA has outsourced most R&T relevant to this Challenge.

Mission Alignment (3): This Challenge is not well aligned with NASA’s mission. Relevant tools and methodologies are applicable to any complex system, not just aerospace.

Lack of Alternative Sponsors (1): Industry, DoD, and academia are developing many tools relevant to this Challenge.

Appropriate Level of Risk (1): This Challenge faces very low risk. Many relevant tools and techniques exist and much of the basic research has been done.

REFERENCES

Bokadia, S., and J. Valasek. 2001. Severe Weather Avoidance Using Informed Heuristic Search. AIAA-2001-4232, AIAA Guidance, Navigation, and Control Conference, Montreal, Canada, August 6-9.

Chavez, F.R., and D.K. Schmidt. 1994. Analytical aeropropulsive-aeroelastic hypersonic-vehicle model with dynamic analysis. Journal of Guidance, Control, and Dynamics 17(6): 1308-1319.

Ding, Y., J. Rong, and J. Valasek. 2004. Feasibility Analysis of Aircraft Landing Scheduling for Non-Controlled Airports. AIAA-2004-5241, Proceedings of the AIAA Guidance, Navigation, and Control Conference, Providence, R.I., August 16-19.

Doebbler, J., P. Gesting, and J. Valasek. 2005. Real-Time Path Planning and Terrain Obstacle Avoidance for Aircraft. AIAA-2005-5825, Proceedings of the AIAA Guidance, Navigation, and Control Conference, San Francisco, Calif., August 15-18.

Garg, S. 2005. NASA Glenn Research in Controls and Diagnostics for Intelligent Aerospace Propulsion Systems. NASA/TM—2005-214036, Glenn Research Center, Cleveland, Ohio, December.

Glauert, M.B. 1945. The design of suction aerofoils with very large CL-range. Aeronautics Research Council Reports and Memoranda 2111, National Research Establishment, November.

Glauert, M.B., W.S. Walker, W.G. Raymer, and N. Gregory. 1948. Windtunnel tests on a thick suction aerofoil with a single slot. Aeronautics Research Council Reports and Memoranda 2646, National Research Establishment, October.

Helbing, K., T. Spaeth, and J. Valasek. Forthcoming. Improving aircraft sequencing and separation at a small aircraft transportation system airport. Journal of Aircraft.

Jaw, L.C., and S. Garg. 2005. Propulsion Control Technology Development in the United States: A Historical Perspective. NASA/TM-2005-213978, October.

Kelly, W., J. Valasek, D.Wilt, J. Deaton, K. Alter, and R. Davis. 2005. The Design and Evaluation of a Traffic Situation Display for a SATS Self Controlled Area. Proceedings of the 24th Digital Avionics Systems Conference, Washington, D.C., October 30-November 3.

Lampton, A., and J. Valasek. 2005. Prediction of Icing Effects on the Stability and Control of Light Airplanes. AIAA-2005-6219, Proceedings of the AIAA Atmospheric Flight Mechanics Conference, San Francisco, Calif., August 15-18.

Lampton, A., and J. Valasek. 2006. Prediction of Icing Effects on the Lateral/Directional Stability and Control of Light Airplanes. AIAA-2006-6487, Proceedings of the AIAA Atmospheric Flight Mechanics Conference, Keystone, Colo., August 21-24.

Litt, J.S., D.L. Simon, S. Garg, T. Guo, C. Mercer, R. Millar, A. Behbahani, A. Bajwa, and D.T. Jensen. 2005. A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems. NASA/TM-2005-213622, ARL-TR-3413, May.

Rong, J., S. Geng, J. Valasek, and T. Ioerger. 2002. Air Traffic Conflict Negotiation and Resolution Using An Onboard Multi-Agent System. DASC-345, 21st Digital Avionics Systems Conference (DASC) on Air Traffic Management for Commercial and Military Systems, Irvine, Calif., October 22.

Schierman, J.D., D.G. Ward, J.R. Hull, N. Gandhi, M.W. Oppenheimer, and D.B. Doman. 2004. An approach to integrated adaptive guidance and control with flight test results. Journal of Guidance, Control and Dynamics 27(6).

Tandale, M.D., and J. Valasek. 2003. Structured Adaptive Model Inversion Control to Simultaneously Handle Actuator Failure and Actuator Saturation. AIAA-2003-5325, AIAA Guidance, Navigation, and Control Conference, Austin, Tex., August 11-14.

Tandale, M.D., and J. Valasek. 2006. Fault tolerant structured model inversion control. Journal of Guidance, Control, and Dynamics 29(3).

Tandale, M.D., J. Valasek, J. Doebbler, and A.J. Meade. 2005. Improved Adaptive-Reinforcement Learning Control for Morphing Unmanned Air Vehicles. AIAA-2005-7159, Proceedings of the AIAA Infotech@Aerospace Conference, Arlington, Va., September 26-29.

Valasek, J., M.D. Tandale, and J. Rong. 2005. A reinforcement learning-adaptive control architecture for morphing. Journal of Aerospace Computing, Information, and Communication 2(5): 174-195.



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