Abstract
This paper presents a gain tuning method to obtain optimal gains in designing a 2-DOF-PID controller. A 2 degree-of-freedom PID controller has been designed to compensate the effects of disturbance without degrading tracking performance. But it is not easy to get optimal gains and keep system stability during the tuning process with real-time tuning methods. Thus we suggest an offline tuning method by genetic algorithm. For the experimental system we consider a commercial sewing machine that requires high speed, robustness and accuracy. Modeling techniques based on neural networks have proven to be quite useful for building good quality models from measured data. So it is modeled by a neural network that is configured as an output-error dynamical system. Then using the model, we search optimal gains for 2-DOF-PID controller with genetic algorithm in offline simulation. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective.
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