Prof. Peter Seiler
University of Minnesota
Department of Aerospace Engineering and Mechanics
The FAA (US) and JAA (European) certification authorities impose high demands on the reliability of safety-critical aircraft systems. The high degree of reliability is typically achieved through the use of physically redundant components. Commercial aircraft such as the Boeing 777 have redundant control surfaces, sensors, processors, hydraulic lines, and communication networks. This talk will first review the design challenges associated with redundancy management for commercial aircraft. One drawback of physical redundancy is the increased size, cost, weight, and power requirements. Some aircraft, e.g. unmanned aircraft, cannot be designed to meet the conflicting design requirements imposed by the use of physical redundancy for reliability. Model-based fault detection provides an alternative means to achieve high levels of reliability without using redundant physical hardware. These analytical methods detect faults using dynamic models to relate the behavior of various subsystems and sensor measurements. A major obstacle to the use of analytical fault detection in aerospace systems is the lack of appropriate tools to analyze and certify the performance of these systems. The second part of the talk will describe a mathematical framework that can be used to analyze the performance of safety-critical systems that rely on analytical redundancy.
Dr. Seiler received his Ph.D. from the University of California, Berkeley in 2001. His graduate research focused on coordinated control of unmanned aerial vehicles and control over wireless networks. From 2004-2008, Dr Seiler worked at the Honeywell on various aerospace and automotive applications including the redundancy management system for the Boeing 787, sensor fusion algorithms for automotive active safety systems and re-entry flight control laws for NASA’s Orion vehicle. Since joining the University of Minnesota in 2008, Dr. Seiler has been working on model-based fault-detection methods that can be applied for safety-critical systems. He is also investigating advanced multivariable control strategies for wind turbine control.