On-line tuning of a single EWMA controller based on the neural technique

Abstract

The exponentially weighted moving average (EWMA) controller has been proven to be an effective algorithm in the control the modern manufacturing system. The performance of the EWMA controlled process is based on choosing the correct EWMA gain. Most related research has focused on analysing the optimal EWMA gain in the static condition. The objective was to propose an approach based on the neural technique for on-line tuning of the single EWMA gain. The underlying approach indicated that the network learns very quickly when taking autocorrelation function and sample partial autocorrelation function patterns as the input features. It is shown that the sequence of the EWMA gains, generated by the proposed adaptive approach, converges close to the optimal controller value under several disturbance models, including IMA(1,1), and step and small ramp disturbances. In addition, the approach possesses a superior controlled output performance compared with the previous adaptive system.