The method is well suited in situations where we know a stabilizing controller of a nominal reduced order model, which we would like to robustify with respect to high order dynamics.
The construction of the stabilising control laws resembles the procedure used in nonlinear regulator theory to derive the (invariant) output-zeroing manifold and its friend, and is well suited, for instance, in situations where we know a stabilising controller of a nominal reduced-order model, which we would like to robustify with respect to higher-order dynamics.
In parallel, a nominal model was learned from vehicles which complied with the traffic rules.
Since the aircraft is weakly minimum phase, a zero filter was used in the adaptive autopilot, which required a nominal model of the linearized plant at the design condition.
Using this measure, a robust controller design approach, which is based on a nominal model, is proposed.
The dynamics of the adaptive structure changes following the deformation, which is described by a nominal model and a perturbation term.
For "ideal" feedforward controllers based on the inverted nominal model, the feedforward sensitivities equal the relative model errors, which must thus remain less than 1 for feedforward control to have a positive (dampening) effect.
The alignment is an iterative process involving nonlinear constrained optimization, which forces datapoints to lie outside the nominal model under a specific order of priority.
Based on the nominal model, a robust controller is designed via the sliding-mode variable structure approach, which is derived in the sense of Lyapunov stability theorem.
We would want to discriminate between these "sets" of models (which represent signalling pathway structures) but since this is a very hard problem, our method uses a nominal model from each set and then designs an experiment in order to discriminate between these nominal models.
