  Robust control design is mainly devoted to guarantee closed-loop stability of
a model-based control law in presence of parametric and structural
uncertainties. The control law is usually a complex feedback law which is
derived from a (nonlinear) model, possibly complemented with some mathematical
envelope of the model uncertainty. Stability may be guarantee with the help of
some ignorance coefficients and restricting the feedback control effort with
respect to the model-based design. Embedded Model Control shows that under
certain conditions, the model-based control law must and can be kept intact
under uncertainty, if the controllable dynamics is complemented by a suitable
disturbance dynamics capable of real-time encoding the different uncertainties
affecting the 'embedded model', i.e. the model which is both the design source
and the core of the control unit. To be real-time updated the disturbance state
is driven by an unpredictable input vector, called noise, which can be only
estimated from the model error. The uncertainty (or plant)-based design
concerns the noise estimator, as the model error may convey into the embedded
model uncertainty components (parametric, cross-coupling, neglected dynamics)
which are command-dependent and thus prone to destabilize the controlled plant.
Separation of the components into the low and high frequency domain by the
noise estimator allows to recover and guarantee stability, and to cancel the
low frequency ones from the plant. Among the advantages, control algorithms are
neatly and univocally related to the embedded model, the embedded model
provides a real-time image of the plant, all control gains are tuned by fixing
closed-loop eigenvalues. Last but not least, the resulting control unit has
modular structure and algorithms, thus facilitating coding. A simulated case
study helps to understand the key assets of the methodology.
