### Abstract

A neural network controller is described for controlling unknown, linear, discrete-time CARMA systems with single-input single-output. A linear two-layered neural network is used to model the inverse dynamics of the unknown plant on-line; it is learned by the delta rule, in which the difference between the actual control input to the plant, which is generated from the neural controller, and the input estimated from the inverse-dynamics model by using an actual plant output is minimized. A similar neural network is also used to estimate the unknown noise sequence so that the proposed neural network controller can treat a noisy output, where the regular dynamics are modelled on-line by using the actual plant output. Some simulation examples are finally presented to illustrate the features of the present neural controller.

Original language | English |
---|---|

Pages (from-to) | 483-497 |

Number of pages | 15 |

Journal | International Journal of Control |

Volume | 56 |

Issue number | 2 |

DOIs | |

Publication status | Published - 1992 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Control and Systems Engineering
- Computer Science Applications

### Cite this

*International Journal of Control*,

*56*(2), 483-497. https://doi.org/10.1080/00207179208934324

**An adaptive control for CARMA systems using linear neural networks.** / Watanabe, Keigo; Fukuda, Toshio; Tzafestas, Spyros G.

Research output: Contribution to journal › Article

*International Journal of Control*, vol. 56, no. 2, pp. 483-497. https://doi.org/10.1080/00207179208934324

}

TY - JOUR

T1 - An adaptive control for CARMA systems using linear neural networks

AU - Watanabe, Keigo

AU - Fukuda, Toshio

AU - Tzafestas, Spyros G.

PY - 1992

Y1 - 1992

N2 - A neural network controller is described for controlling unknown, linear, discrete-time CARMA systems with single-input single-output. A linear two-layered neural network is used to model the inverse dynamics of the unknown plant on-line; it is learned by the delta rule, in which the difference between the actual control input to the plant, which is generated from the neural controller, and the input estimated from the inverse-dynamics model by using an actual plant output is minimized. A similar neural network is also used to estimate the unknown noise sequence so that the proposed neural network controller can treat a noisy output, where the regular dynamics are modelled on-line by using the actual plant output. Some simulation examples are finally presented to illustrate the features of the present neural controller.

AB - A neural network controller is described for controlling unknown, linear, discrete-time CARMA systems with single-input single-output. A linear two-layered neural network is used to model the inverse dynamics of the unknown plant on-line; it is learned by the delta rule, in which the difference between the actual control input to the plant, which is generated from the neural controller, and the input estimated from the inverse-dynamics model by using an actual plant output is minimized. A similar neural network is also used to estimate the unknown noise sequence so that the proposed neural network controller can treat a noisy output, where the regular dynamics are modelled on-line by using the actual plant output. Some simulation examples are finally presented to illustrate the features of the present neural controller.

UR - http://www.scopus.com/inward/record.url?scp=0040450028&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0040450028&partnerID=8YFLogxK

U2 - 10.1080/00207179208934324

DO - 10.1080/00207179208934324

M3 - Article

VL - 56

SP - 483

EP - 497

JO - International Journal of Control

JF - International Journal of Control

SN - 0020-7179

IS - 2

ER -