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Can I read Neuro-genetic PID autotuning: time invariant case on EtoBox?
Neuro-genetic PID autotuning: time invariant case by João M.G. Lima; António E. Ruano is a Engineering article available to read on EtoBox.
What is Neuro-genetic PID autotuning: time invariant case about?
The Proportional, Integral and Derivative (PID) controllers are widely used in industrial applications. Their popularity comes from their robust performance and also from their functional simplicity. Because the plant to be controlled is time varying, or due to components ageing, or yet because of changes in process dynamics due to alterations of operation conditions, these controllers need to be regularly retuned. Since an accurate tuning is a time-consuming operation, and as even a single plant can have several of these small controllers, methods that automate the tuning process are economically important. In this paper a recent approach for PID autotuning, involving neural networks, is further extended, to incorporate multiple tuning criteria, and to make use of on-line experimental data. In this paper neural network models of tuning criteria, together with the use of genetic algorithms (GA), are proposed to achieve this aim. Simulation results show that, for the case of time-invariant plants, trained multilayer perceptrons are good models and generalise well. The closed loop unit step response obtained with the neuro-genetic approach compares favourably with the one achieved us
Who reads Neuro-genetic PID autotuning: time invariant case?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- João M.G. Lima; António E. Ruano
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0378-4754)
- Published
- 2000
- Language
- EN
- Field
- Engineering (Physical Sciences)