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 |