Neural networks PID autotuning with real-time adaptation

Despite the developments in more sophisticated controllers, still the Proportional, Integral and Derivative (PID) controller is by far the controller most widely used in industry automation.

Neuro-genetic PID autotuning: time invariant case

The Proportional, Integral and Derivative (PID) controllers are widely used in induxtrial applications. Their popularity comes from their robust performance and also from their functional simplicity.

B-splines neural network assisted PID autotuning

This papers describes an extantion of previous works on the subject of neural network proportional, integral and derivative (PID) autotuning. Basically, neural networks are employed to supply the three PID parameters, according to the integral of time multiplied by the absolute error (ITAE) criterion, to a standard PID controller.

On-line adaptation of neural network

The Proportional Integral and Devirative (PID) controller autotuning is an important problem, both in practical and theoretical terms. The autotuning procedure must take place in real-time, and therefore the corresponding optimisation procedure must also be executed in real-time and without disturbing on-line control.

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.

Application of permeation liquid membrane and scanned stripping chronopotentiometry to metal speciation analysis of colloidal complexes

The potential of permeation liquid membrane (PLM) to obtain dynamic metal speciation information for colloidal complexes is evaluated by measurements of lead(II) and copper(II) complexation by carboxyl modified latex nanospheres of different radii (15, 35, 40 and 65 nm). The results are compared with those obtained by a well characterized technique: stripping chronopotentiometry at scanned deposition potential (SSCP). Under the PLM conditions employed, and for large particles or macromolecular ligands, membrane diffusion is the rate-limiting step. That is, the flux is proportional to the free metal ion concentration with only a small contribution from labile complexes. In the absence of ligand aggregation in the PLM channels, good agreement was obtained between the stability constants determined by PLM and SSCP for both metals.