Abstract
Nine cooperating rule-based systems, collectively called AUTOCREW, were designed to automate functions and decisions associated with a combat aircraft's subsystems. Performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Each AUTOCREW subsystem is composed of several expert systems that perform specific tasks. The NAVIGATOR determines optimal navigation strategies from a set of available sensors. A navigation sensor management (NSM) expert system was systematically designed from Kalman filter covariance data: The NSM Expert was developed by means of the analysis-of-variance (ANOVA) and ID3 algorithm. Navigation strategy selection is based on a root-sum-of-squares position error decision metric computed from the covariance data. Results show that the NSM Expert predicts position error correctly between 45% and 100% of the time for a specified navaid configuration and aircraft trajectory. The NSM Expert adapts to new situations and provides reasonable estimates of hybrid performance. The systematic nature of the ANOVA/ID3 method makes it broadly applicable to expert system design when experimental or simulation data are available.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2191-2197 |
| Number of pages | 7 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 4 |
| State | Published - 1990 |
| Event | Proceedings of the 29th IEEE Conference on Decision and Control Part 6 (of 6) - Honolulu, HI, USA Duration: Dec 5 1990 → Dec 7 1990 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization