Abstract
A decentralized multiple model adaptive filter (MMAF) is proposed for linear discrete-time stochastic systems. The structure of decentralized multiple model studied here is based on introducing a global hypothesis for the global model and a local hypothesis for the local model, where it is assumed that the former hypothesis includes the latter one as a partial element. Algorithms for the decentralized MMAFs in unsteady and steady-state are derived using recent results in decentralized Kalman filtering. The results can be applied in designing a system for sensor failure detection and identification (FDI). An example is included to illustrate the characteristics of such a FDI system for the estimation of lateral dynamics of the hydrofoil boat.
| Original language | English |
|---|---|
| Pages (from-to) | 371-377 |
| Number of pages | 7 |
| Journal | Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME |
| Volume | 111 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Sept 1989 |
| Externally published | Yes |
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Instrumentation
- Mechanical Engineering
- Computer Science Applications