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**Design of linear quadratic adaptive control: theory and algorithms for practice.**
*(English)*
Zbl 0586.93040

Kybernetika 21, Suppl. 2, 97 p. (1985).

A linear quadratic gaussian adaptive digital controller based on a recursively identified (multivariate) regression model is presented. The paper develops ideas which are orientated to be used in practical application. All the ideas which have a certain influence on the performance on adaptive optimal algorithms are involved in the study leading to the design of an optimal discrete controller. In this way, the concepts of appropriate statistics, planning optimization horizon (including receding horizon), multiple sampling (for input and output), numerical stability of calculations, the management with simple control laws in a wide range of set points owing to the self-tuning properties, and penalty functions for large input/output deviations from set-points values are examined. The implementation is discussed on the basis of a universal software support design including standard subroutines with automatic initialization (for instance, for data processing). The advantages and inconveniences of each design element are pointed out. A set of examples of industrial designs to which these techniques were applied (drum boiler, cold-rolling mill, etc.) are presented, and the related obtained results are discussed.

Reviewer: M.de la Sen

### MSC:

93C40 | Adaptive control/observation systems |

93C57 | Sampled-data control/observation systems |

93E12 | Identification in stochastic control theory |

93E25 | Computational methods in stochastic control (MSC2010) |

93C05 | Linear systems in control theory |

93E03 | Stochastic systems in control theory (general) |

93E11 | Filtering in stochastic control theory |

### Keywords:

linear quadratic gaussian adaptive digital controller; regression model; planning optimization horizon; multiple sampling### References:

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