Loading...
5 results
Search Results
Now showing 1 - 5 of 5
- A neural network PID autotunerPublication . Ruano, Antonio; Lima, João; Mamat, R.; Fleming, P. J.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.
- Comparison of alternative approaches to neural network PID autotuningPublication . Ruano, Antonio; Lima, João; Mamat, R.; Fleming, P. J.In this paper, a scheme for the automatic tuning of PID controllers on-line, with the assistance of trained neural networks, is proposed. The alternative approaches are presented and compared.
- Um modelo em simulink para sintonia automática de controladores PID usando redes neuronaisPublication . Lima, João; Ruano, Antonio; Mamat, R.; Fleming, P. J.The PID controllers are widely used in industry. Whether because the plant is time-varying, or because of components ageing, these controllers need to be regularly retuned. During the last years, several methods have been proposed for PID autotuning.
- Automatic tuning of PID controllers using a neuro-genetic systemPublication . Ruano, Antonio; Lima, João; Azevedo, Ana Beatriz da Piedade de; Duarte, N. M.; Fleming, P. J.Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.
- A novel technique for controller tuningPublication . Ruano, Antonio; Lima, João; Azevedo, Ana Beatriz da Piedade de; Duarte, N. M.; Fleming, P. J.Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller tuning problems. In this paper we purpose to combine its joint use, by exploiting the nonlinear mapping capabilities of neural networks to model objective functions, and use them to supply their values to a genetic algorithm which performs on-line minimization. Simulation results show that this is a valid approach, offering desired properties for on-line use such as a dramatic reduction in computation time and avoiding the need of perturbing the closed-loop operation.