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- 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.
- 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.