Detail publikace

Quantization Effect Influences Identification in Adaptive LQ Controller

Originální název

Quantization Effect Influences Identification in Adaptive LQ Controller

Anglický název

Quantization Effect Influences Identification in Adaptive LQ Controller

Jazyk

en

Originální abstrakt

Many controlling algorithms were developed to satisfy designer’s assumption that controlled process is stable, optimal, adaptive etc. Only few of them should be used in the real-time, on-line adaptation or implemented into Programmable Logic Controller. Algorithms based on neural networks were successfully tested as for the process control as for the process identification. In simulation, quantization effect given by A/D and D/A converter is very often left out. Quantization effect influences identified process transfer function and controller possibilities. In this case, controller controls the process inaccurate and often with oscillations than without quantization. This paper shows a comparison between two identification methods. Online identification (in the real time) based on neural networks and a classical identification are implemented in adaptive LQ controller with three types of A/D and D/A converters at least be closer in simulation the real process controlling.

Anglický abstrakt

Many controlling algorithms were developed to satisfy designer’s assumption that controlled process is stable, optimal, adaptive etc. Only few of them should be used in the real-time, on-line adaptation or implemented into Programmable Logic Controller. Algorithms based on neural networks were successfully tested as for the process control as for the process identification. In simulation, quantization effect given by A/D and D/A converter is very often left out. Quantization effect influences identified process transfer function and controller possibilities. In this case, controller controls the process inaccurate and often with oscillations than without quantization. This paper shows a comparison between two identification methods. Online identification (in the real time) based on neural networks and a classical identification are implemented in adaptive LQ controller with three types of A/D and D/A converters at least be closer in simulation the real process controlling.

BibTex


@inproceedings{BUT7879,
  author="Kamil {Švancara} and Petr {Pivoňka}",
  title="Quantization Effect Influences Identification in Adaptive LQ Controller",
  annote="Many controlling algorithms were developed to satisfy designer’s assumption that controlled process is stable, optimal, adaptive etc. Only few of them should be used in the real-time, on-line adaptation or implemented into Programmable Logic Controller. Algorithms based on neural networks were successfully tested as for the process control as for the process identification. In simulation, quantization effect given by A/D and D/A converter is very often left out. Quantization effect influences identified process transfer function and controller possibilities. In this case, controller controls the process inaccurate and often with oscillations than without quantization. This paper shows a comparison between two identification methods. Online identification (in the real time) based on neural networks and a classical identification are implemented in adaptive LQ controller with three types of A/D and D/A converters at least be closer in simulation the real process controlling.",
  address="Kostas J. Kyriakopoulos, National Technical University of Athens, Greece",
  booktitle="The 11th Mediterranean Conference on Control and Automation",
  chapter="7879",
  institution="Kostas J. Kyriakopoulos, National Technical University of Athens, Greece",
  year="2003",
  month="june",
  pages="77",
  publisher="Kostas J. Kyriakopoulos, National Technical University of Athens, Greece",
  type="conference paper"
}