• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.1010-1016
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• 2004

In this paper, a scheme for estimating the parameters of solar cells and a NN-based emulator for predicting the maximum power point are presented. The diode model with series and shunt resistors is used to estimate parameters highly affecting its V-I characteristic curve and both a real-coded genetic algorithm and the model adjustment technique are employed. For implementing the emulator, a multi-layered neural network incorporating with the BP algorithm is used. A set of simulation works using both field data and generated data are carried out to demonstrate the effectiveness of the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.24-32
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• 2003

The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.