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According to the model matching condition, an update scheme is used in the adaptive control part to eliminate system errors caused by model mismatch.
To deal with the uncertainty of plant-model matching conditions needed for adaptive control, multiple reference model systems are employed to generate multiple parameter estimation and feedback control signals from which a most suitable control input is selected by a control switching mechanism designed using multiple estimation errors.
Such a control scheme is useful when the plant-model matching conditions for state tracking cannot be satisfied.
This paper develops a multivariable multiple-model adaptive control scheme for adaptive state feedback state tracking control of systems whose plant-model matching conditions are uncertain and parameters are unknown.
With such a control scheme, the plant-model matching conditions are much less restrictive than those for state tracking, while the controller has a simpler structure than that of an output feedback design.
From these preliminary experimental results, we see that the recognition performance can be improved with the collection of single condition models, when a single-condition model matches the run-time condition very well.
The experiments were done by using causal reverberation model (using left context only) and non-causal reverberation model (using left and right context) on known environments (matched conditions).
The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single utterance basis.
The asterisks indicate known positions (matched conditions).
Furthermore, the ultra-precision turning and milling experiments were conducted, and the theoretical model matched the experiments well within normal operating conditions.
With respect to fracture in multi-fiber composites, the proposed model matches theoretical predictions of post-cracking strength and pullout displacement corresponding to the load-free condition.
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