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• W162678072 abstract "Abstract The paper considers the adaptive control of discrete-time, noise corrupted, systems whose characteristics can be represented by means of an ARMA model, this being posed within a canonical state-space framework. The effects of using different state vector forms, on self-tuning controller performance, are investigated with regard to filtered and predicted state representations when applied to various system types. It is shown how only the optimal observer for the state-space filter model is truly optimal in the sense of being a constituent part of an optimal controller. The important aspect of these results is that the state-space prediction model, also known as the innovations model, is that which is widely used in practice, despite the fact that overall optimality conditions cannot be obtained with a state-feedback controller based on this state-space model. The suggestion is made that the state space filter model, also known as the noise-free measurement model, is a much more viable alternative." @default.
• W162678072 created "2016-06-24" @default.
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• W162678072 date "1990-04-01" @default.
• W162678072 modified "2023-09-27" @default.
• W162678072 title "Filtered and Predicted States for Discrete-Time Adaptive Control" @default.
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• W162678072 doi "https://doi.org/10.1016/s1474-6670(17)52708-1" @default.
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