Adaptative high-gain extended Kalman filter and applications

tel-00559107, version 1 - 24 Jan 2011
1. the innovation at time t places an upper bound on the error at time t − d,
3.9 Conclusion
2. the Riccati matrix S is bounded from above and below, for all times t, independently
from θ,
3. the set of candidate adaptation functions is non empty.
In the next chapter, the description of the observer is completed with an adaptation
function and the analysis of an example. This study is done both in a simulation and in
a real process. We also propose guidelines to the tuning of the several parameters of the
observer.
55