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This study detects oscillations in the control loop and separates them from others by implementing supervised machine learning on generalized and normalized statistical variables. Oscillations in the control loops can result in high variability of performance, increase the costs, increment defects and potential hazards in the future. Valve stiction is one of the most important reasons for oscillatory behaviour in the process industry. The detection of this non-linear parameter becomes even more complex in the presence of other oscillating factors, such as poor controller tuning and external disturbances. The proposed method is based on a six-step algorithm. After preparing the data, the best classifier is selected from three trained classifiers Naïve Bayes, support vector machine and K -nearest neighbours' separators. Finally, the decision tree will automatically detect and classify oscillating factors in the control loop. This method is independent of the process model, and through the decision tree, it determines the probability of occurrence of each oscillation factor in the loop. The resulting system was tested on benchmark industrial data to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new robust control strategy is proposed to regulate the output voltage of a quasi‐resonant converter. The proposed approach completely eliminates the switching losses and the electromagnetic interferences due to high current changing rates. The circuit structure is simple and affordable and does not have many auxiliary switches. The regulated output voltage of a buck converter must be robust to input voltage variations. Moreover, the controller must be robust to the circuit parameter uncertainties. In this paper, a new control strategy with nonlinear structure is used with the advantage of robustness, and turn‐on and turn‐off zero current switching. The robustness of the converter to the input voltage variations and the parameter uncertainties is investigated. The new proposed control strategy can be implemented using different controller types. Therefore, a comparison between the proposed nonlinear controller and a conventional PI controller, which is most widely used in practice, is performed. The simulation results show the efficiency and robustness of the proposed method. Finally, the simulation results are verified by the experimental tests. [ABSTRACT FROM AUTHOR]