Determining Configuration Probabilities of Safety-Critical Adaptive Systems

2007 ◽  
Author(s):  
Rasmus Adler ◽  
Marc Forster ◽  
Mario Trapp
Keyword(s):  
Adaptive Systems ◽  
Safety Critical

Aviation Psychology – Applied Methods and Techniques

2021 ◽  
Author(s):  
Sonja Biede-Straussberger
Keyword(s):  
Safety Culture ◽  
Adaptive Systems ◽  
Applied Research ◽  
Diverse Range ◽  
Safety Critical ◽  
Spatial Disorientation ◽  
Psychological Tools ◽  
Aviation Psychology ◽  
Methods And Techniques ◽  
Cardiac Assessment

This collection of chapters on the latest methods and tools for applied research in aviation psychology guides the diverse range of professionals working within aviation on how to adapt flexibly to the continuously evolving requirements of the aeronautical landscape. Experts from the industry and academia explore selected applications, ranging from aviation system engineering to bridging the gap between research and industrialization, safety culture, training and examination. Psychological tools are explored, including designing biocybernetic adaptive systems, predictive automation, and support for designing the human role in future human–machine teaming concepts. Special chapters are dedicated to spatial disorientation, reactivity, stress, eye-tracking, electrodermal and cardiac assessment under the influence of G forces. This is essential reading for aviation psychologists, human factors practitioners, engineers, designers, operational specialists, students and researchers in academia, industry, and government. The practitioners and researchers working in other safety critical domains (e.g., medicine, automotive) will also find the handbook valuable.

Adaptive control of time-varying non-linear plants by speed-gradient algorithms

2019 ◽  
pp. 37-44 ◽  
Author(s):  
O. P. Tomchina ◽  
D. N. Polyakhov ◽  
O. I. Tokareva ◽  
A. L. Fradkov
Keyword(s):  
Adaptive Control ◽  
Adaptive Systems ◽  
Reference Model ◽  
Maximum Deviation ◽  
Model Parameters ◽  
Time Varying ◽  
System Solution ◽  
Non Linear ◽  
Initial Uncertainty ◽  
Speed Gradient

Introduction: The motion of many real world systems is described by essentially non-linear and non-stationary models. A number of approaches to the control of such plants are based on constructing an internal model of non-stationarity. However, the non-stationarity model parameters can vary widely, leading to more errors. It is only assumed in this paper that the change rate of the object parameters is limited, while the initial uncertainty can be quite large.Purpose: Analysis of adaptive control algorithms for non-linear and time-varying systems with an explicit reference model, synthesized by the speed gradient method.Results: An estimate was obtained for the maximum deviation of a closed-loop system solution from the reference model solution. It is shown that with sufficiently slow changes in the parameters and a small initial uncertainty, the limit error in the system can be made arbitrarily small. Systems designed by the direct approach and systems based on the identification approach are both considered. The procedures for the synthesis of an adaptive regulator and analysis of the synthesized system are illustrated by an example.Practical relevance: The obtained results allow us to build and analyze a broad class of adaptive systems with reference models under non-stationary conditions.

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