In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties together with the noise level are analyzed. The effectiveness of the proposed approach is validated by some experimental tests.

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.

In this work, issues related to the application of the D-decomposition technique to selection of the controller parameters for a drive system with flexibility are presented. In the introduction the commonly used control structures dedicated to two-mass drive systems are described. Then the mathematical model as well as control structure are introduced. The considered structure has only basic feedbacks from the motor speed and PI type controller. Due to the order of the closed-loop system, the free location of the system’s poles is not possible. Large oscillations can be expected in responses of the plant. In order to improve the characteristics of the drive, the tuning methodology based on the D-decomposition technique is proposed. The initial working point is selected using an analytical formula. Then the value of controller proportional gain is decreasing, until the required value of overshoot is obtained. In the paper different advantages of the D-decomposition technique are presented, for instance calculation of global stability area for the selected gain and phase margin, the impact of parameter changes, and additional delay evident in the system. Theoretical considerations are confirmed by simulation and experimental results.

Model Reference Adaptive Systems (MRASs) are one of the most commonly applied techniques to estimate induction motor speed. They ensure simple and algorithmic way to obtain all necessary variables to create a sensorless drive, or to provide the estimated speed signal for redundant system. However, one of the most popular MRAS-type estimators, MRAS-CC (C – Current estimator, C – Current flux simulator) is unstable in a large part of the regenerating mode operation. In this paper it is proposed to apply the multilayer observer theory to ensure the stability, while preserving large insensitivity to motor parameters. The proposed MRAS-ML (ML – MultiLayer) estimator includes two different MRAS-type estimators, unlike previous multilayer solutions with multiple identical observers, but with different parameters. Additionally, no auxiliary parameters need to be selected and it is not necessary to switch off any additional adaptation mechanism. It is shown that the MRAS-ML is stable in the whole regenerating mode operation using both simulation and experimental tests.

The main purpose of this work is to present a robust speed control structure for a two-mass system. The tested system consists of a PI controller with two additional feedback. The coefficients of the control system are selected using a pattern-search optimization method in order to obtain robustness to changes in the system parameters. The control system requires information about non-measurable state variables. For this purpose, it is proposed to use a multilayer observer. In order to show the advantages of the MLO system, this article also presents comparative studies with a classical observer. A number of simulation and experimental tests are carried out. The obtained results confirmed a much higher quality of control in the system cooperating with a multilayer observer compared to the system with a classical observer.

This paper proposes a new estimation algorithm based on the Luenberger observer methodology and multilayer concept. The proposed multi-layer Luenberger observer (MLO) is implemented in the control structure designated for a two-mass system. Two types of aggregation mechanism are evaluated in the paper. The MLO ensures better estimation quality of the mechanical state variables: motor speed, shaft torque, load speed and load torque, as compared to the classical single observer. The more accurate estimated states, the more precise closed-loop control is guaranteed. MLO is designated for the system where initial conditions of the plant are not known or the state variables can change rapidly (load torque in the considered case). The estimation algorithm and control strategy is evaluated through simulation and experimental tests. The obtained results confirm efficiency of the proposed MLO.