Neural networks assisted PID autotuning

The Proportional, Integral and Derivative (PID) controllers are standard building blocks for industrial automation. Their popularity comes from their rebust performance and also from their functional simplicity.

Neural networks assisted PID autotuning

A scheme of automatically tuning the existing industrial PID controllers using neural networks is proposed. The scheme estimates the process critical data on-line in proportional control mode.

Connectionist approach to PID autotuning

Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. The popularity of these regulatores comes from their rebust performance in a wide range of operationg conditions, and also from their functional simplicity, which makes them suitable for manual tuning.

A neural network PID autotuner

Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. Their popularity comes from their rebust performance and also from their functional simplicity. Whether because the plant is time-varying, or because of components ageing, these controllers need to be regularly retuned.

A connectionist approach to PID autotuning

Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. The popularity of these regulators comes from their rebust performance in a wide range of operating conditions, and also from their functional simplicity, which makes them suitable for manual tuning.

A neural network controller

Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. The popularity of these regulators comes from their rebust performance in a wide range of operating conditions, and also from their functional simplicity, which makes them suitable for manual tuning.