A double neural network self-tuning pid control method for integrated anti-rolling device based on energy optimization
An anti-rolling device and neural network technology, applied in transportation and packaging, equipment to reduce ship movement, equipment to increase stability of ships, etc., can solve problems such as unconsidered optimization time, achieve good control effects, save time and The effect of saving navigation cost and saving optimization time
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[0055] The present invention is described in more detail below in conjunction with accompanying drawing example:
[0056] combine Figure 1-16 , the present invention comprises the following steps:
[0057] (1) Establish the integrated anti-rolling system model, and take the wave dip angle as the input of the integrated anti-rolling system.
[0058] (2) Create performance indicators based on the established integrated anti-rolling system model. The performance indicators mainly include roll angle variance, fin angle saturation rate and energy consumption of driving fin stabilizer system.
[0059] (3) Adjust the parameter K of the PID controller online by using the double neural network P 、K I and K D , to realize PID parameter self-tuning. In order to optimize the performance index of the integrated anti-rolling system established in the second step.
[0060] (4) Under real-time sea conditions, a delay link is added to the PID controller. The delay time is adjusted in r...
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