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This paper presents an application of an Unscented- and a Fuzzy Unscented- Kalman Filter (UKF and FUKF) to the estimation of mechanical state variables and parameters in a drive system with an elastic connection. The cascade control structure incorporating an IP controller supported by two additional feedbacks and suitable adaptation mechanism is investigated in this study. The coefficients of the control structure are retuned on the basis of the value of mechanical parameters estimated by filter. The effectiveness of the proposed approaches (classical and fuzzy) is researched through simulation and experimental tests.

PurposeThe aim of the research was to find out a method of adaptive speed control robust against variation of selected parameters of system like moment of inertia, time constant of torque control loop or torque coefficient of the motor.Design/methodology/approachThe main goal of the research was achieved due to application of artificial neural network (ANN), which was trained on line on the base of speed control error. The good results were gained by elaboration of enough fast and precise training algorithm and proper ANN structure.FindingsThe work shows a structure of artificial neural network (ANN), applied as adaptive speed controller, and presents an algorithm of ANN training. Some versions of this algorithm were analysed and verified by simulation and experimental tests.Research limitations/implicationsThe research should be continued to determine a final version of training algorithm and its influence on controller properties.Practical implicationsThe elaborated adaptive controller can be easily used by applying microprocessor system available now on the market. The proposed control solution is robust against parameters variation as well as their imprecise identification. The controller has ability of self‐tuning which can have great practical advantage.Social implicationsSocial implications are difficult to determine.Originality/valueThe paper presents a new solution of adaptive speed controller, which means a new ANN structure and new training algorithm.

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PurposeThe aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approachThe robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. The control algorithms of the system are performed by a microprocessor floating point DSP control system.FindingsThe proposed controller structure with proper control surface created by ANN guarantees expected robustness.Originality/valueThe original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

In the paper, a model of a three-phase frequency-controlled induction electric drive has been developed in Simulink Matlab 2019 (MathWorks, Natick, MA, USA). This model is mathematically converted into a two-phase model by transforming equations. It is proposed to compensate the voltage drop in the power system during start-up operation under load by using supercapacitors as a buffer power source. A block of supercapacitors was calculated. Simulation modeling was performed at a different voltage than the network. The diagrams of the transient processes occurring in the electric drive when the power supply is changed were prepared. It was found that such a principle of implementing an additional source of electric energy allows to start induction electric drives in areas remote from industrial networks without significantly affecting their static and dynamic characteristics.

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