The design of a Fractional-Order fuzzy-based PID (FO-F-PID) control structure is presented for Buck converter in the presence of harmful disturbances. A fractional-order proportional-integral-derivative (FO-PID) control scheme is utilized initially to damp the oscillations and remove the steady-state error. To increase the tendency rate of the error to zero, the FO-PID method is applied to a fuzzy-logic-based compensatory stage. At the same time, the fuzzy part gathers the data based on the error and error derivative. The FO-PID control scheme has the capability to enhance the robustness of the control technique against disturbances and parametric variations. Furthermore, to optimize the control parameters, an efficient algorithm so-called Antlion Optimization (ALO) algorithm, is used. Utilizing the ALO algorithm for tuning the FO-PID gains depicts more accurate responses in solving constrained problems with diverse search spaces. Considering numerous disturbances on DC-DC converters, an FO-F-PID controller can be an appropriate alternative since it is more robust against load variations and noise. Moreover, PSO-PID and FO-PSO-PID controllers are designed to drive a comparison between them. Finally, the merits of the presented controller are validated for various scenarios. It can be seen that the FO-F-ALO-PID method provides much better results with faster dynamics. Matlab-Simulink environment is used for the simulations, and the experimental results are tested by the micro-processor to validate the superiority of the proposed method.

Here, a novel control strategy based on sliding mode control for the single‐stage boost inverter is presented. The goal is to achieve a system with robustness against inherent delays and variations in parameters, fast response, and high‐quality AC voltage. Therefore, according to the idea of current‐mode control, a new type of dynamic sliding mode control (DSMC) is proposed to improve the response performance on various input and parameter operation conditions. In comparison with the conventional controllers, the proposed DSMC utilized only a single loop while presenting attractive features such as robustness against parametric uncertainties and input delay by definition new sliding surfaces. Furthermore, the proposed system has a fast and chattering‐free response, provides an appropriate steady‐state error, good total harmonic distortion (THD), while its implementation is very simple. In a fair comparison with conventional sliding mode control, simulations and laboratory experiments verified satisfactory performance and effectiveness of the DSMC method.

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.

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