Maria Prandini - Politecnico di
Milano
Adaptive self-tuning control describes a
body of approaches where a controller design method based on a system model
is combined with an on-line estimator of the model parameter. The appealing
feature of adaptive controllers consists in their ability to automatically
adjust themselves so as to adapt to the true system. The more commonly adopted strategy for the
design of adaptive control laws is the certainty equivalence approach. Its
success is mainly due to its conceptual simplicity, since it consists in
estimating the unknown parameter via some identification method and then
using the estimate to design the control law as if it were the true value of
the unknown parameter. On the other hand, working out stability and
optimality results for certainty equivalence adaptive control schemes is a
difficult task even in the ideal case when the true system belongs to the
model class. This is due to the intricate interaction between control and
identification in closed-loop, which can cause identifiability problems. The objective of this thesis is twofold:
Such objectives are pursued for linear, time-invariant stochastic SISO
systems affected by white noise based on the infinite-horizon LQG control
design method. Last updated in
February 2007 |