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Repetitive controllers use internal models that provide very high gain at a selected fundamental frequency and its harmonics, additionally, some of the internal models may result unstable, as in the high order repetitive control approach.
To explain the relatively large errors of the internal models in the human nervous system, Maeda et al. (Proceedings of the 1993 International Joint Conference on Neural Networks (IJCNN'93 Nagoya), 1993, pp. 1317 1320) proposed a neural network architecture called independent scalar learning elements summations (ISLES) model.
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This controller design procedure can be interpreted as an approximation of the Internal Model Control technique.
This paper presents a generalization of the Internal Model Control (IMC) approach to feedforward control action generation.
An adaptation algorithm is used to estimate the disturbance frequency and adjusts the parameters of the internal model in the controller.
The main contribution is an alternative approach to the generation of the feedorward control action on the basis of the Internal Model Control formulation.
Nonlinear observability leads to an effective combination of the internal model principle and the certainty equivalence principle for a more general class of systems.
In this paper, an analytical design method is developed for the mismatched Smith predictor on the basis of the internal model control (IMC) method.
The dynamic performance and resisting disturbance capacity of the internal model control is better when the filter time constant is smaller.
First, the existing H∞ servo problem is modified to a structure for the mixed H2/ H∞ control problem by virtue of the internal model principle.
However, a suitable controller design method that is both robust and practicably applicable can be based on the extension of the internal model control scheme.
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