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- 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.
- On-line adaptation of neural networkPublication . Lima, João; Ruano, AntonioThe 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.
- New methods for PID autotuningPublication . Ruano, Antonio; Lima, JoãoIn this paper a recent approach for PID autotuning, involving neural networks, is ferther developed. To make this approach adaptive, optimal PID values must be known on-line. In this paper neural network models of tuning criteria, together with the use of genetic algorithms, are proposed to solve this problem.
- 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.
- Neuro-genetic PID autotuning: time invariant casePublication . Lima, João; Ruano, AntonioThe 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.
- 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.
- Neuro-genetic Pid autotuningPublication . Lima, João; Azevedo, Ana Beatriz da Piedade de; Duarte, N. M.; Fonseca, C. M.; Ruano, Antonio; Fleming, P. J.A new PID autotuning technique, involving neural networks and genetic algorithms is proposed. The validity of this approach is shown, through the results of several experiments. Special attention is given to the off-line training of one of the auto-tuner models, the criterion networks. Procedures used to obtain good training data are described.
- Comparison of off-line and on-line performance of alternative neural network modelsPublication . Lima, João; Ruano, AntonioThe Proportional Integral and Derivative (PID) controller is often used in industrial applications due to its functional simplicity and robust performance. Autotuning methods for these simple controllers are economically important. In order to accomplish this auto tuning in real time, without perturbing the closed-loop operation, models of criteria that are intended to be optimised are needed. In this paper, the ITAE criterion will be employed, as responses obtained with this criterion are well damped. In this paper neural networks are proposed as tools that allow these kinds of mappings. To improve the autotuner performance in a continuous operation, these models should be updated online. This way, the corresponding neural networks, after being trained off-line should be adapted on-line in real time. In the present work, the off-line and on-line performances of Multi-layer Perceptrons (MLPs), Radial Basis Function (RBFs) and Basis-Spline neural networks (B-splines), are analysed and compared.
- 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.