Search

This paper deals with motion control systems with induction motor, subject to severe requirements on both dynamics and steady-state behavior. The proposed control methodology could be viewed as an advancement of the standard field oriented control. It consists of two control loops, i.e., the rotor flux and the speed control loops, designed using the active disturbance rejection control method, with the aim to cope with both exogenous and endogenous disturbances, which are estimated by means of two linear extended state observers and then compensated. Moreover, with the aim of achieving total robustness, a sliding-mode based component is designed, in order to take into account disturbance estimation errors and uncertainties in the knowledge of the control gains. The effectiveness of this approach is shown by means of numerical simulations, and experiments carried out on a suitably developed test set-up. Finally, experimental comparisons between the proposed robust active disturbance rejection control, and the basic active disturbance rejection control are given. [ABSTRACT FROM PUBLISHER]

This article proposes a nonlinear controller based on feedback linearization (FL) for synchronous reluctance motor (SynRM) drives which takes into consideration the magnetic saturation. The proposed nonlinear FL control based control technique has been developed starting from the theoretical definition of an original dynamic model of the SynRM taking into consideration both the self- and the cross-saturation effects. Such a control technique permits the dynamics of both the speed and axis flux loops to be maintained constant independently from the load and the saturation of the iron core in both constant flux and variable direct axis flux operating conditions. Finally, sensitivity of the performance of the proposed FL control versus the variation of the main motor parameters has been verified. The proposed technique has been tested experimentally on a suitably developed test setup. The proposed FL control has been further compared with the classic field-oriented control (FOC) in both constant flux and variable flux working conditions. [ABSTRACT FROM AUTHOR]

This paper deals with robust estimation of rotor flux and speed for sensorless control of motion control systems with an induction motor. Instead of using sixth-order extended Kalman filters (EKFs), rotor flux is estimated by means of a fourth-order descriptor-type robust KF, which explicitly takes into account motor parameter uncertainties, whereas the speed is estimated using a recursive least squares algorithm starting from the knowledge of the rotor flux itself. It is shown that the descriptor-type structure allows for a direct translation of parameter uncertainties into variations of the coefficients appearing in the model, and this improves the degree of robustness of the estimates. Experimental findings, carried out on a closed-loop system consisting of a low-power induction-motor-load system, a proportional–integral-type controller, and the proposed estimator, are shown with the aim of verifying the goodness of the whole closed-loop control system. [ABSTRACT FROM PUBLISHER]

This study proposes a state formulation of the space‐vector dynamic model of the Synchronous Reluctance Motor (SynRM) considering both saturation and cross‐saturation effects. The proposed model adopts the stator currents as state variables and has been theoretically developed in both the rotor and stator reference frames. The proposed magnetic model is based on a flux versus current approach and relies on the knowledge of 11 parameters. Starting from the definition of a suitable co‐energy variation function, new flux versus current functions have been initially developed, based on the hyperbolic functions and, consequently, the static and dynamic inductance versus current functions have been deduced. The dynamic inductance functions have been derived so to fulfill the reciprocity conditions. This study presents also a technique for the estimation of the parameters of the proposed magnetic model, which is based on stand‐still tests without the need to lock the rotor. The identification process has been performed based on the minimization of a suitably defined error function including the difference between the measured and estimated stator fluxes. The proposed parameter estimation technique has been tested in both numerical simulation and experimentally on a suitably developed test set‐up, permitting the experimental validation of the proposed model. [ABSTRACT FROM AUTHOR]

This paper deals with a robust input–output feedback linearization control technique for induction motors. Indeed, classic feedback linearization presents two main disadvantages: 1) the accuracy of the dynamic model; and 2) the corresponding correct knowledge of the model parameters. To address this issue, the linear controller has been substituted with a suitably controller designed to be robust to the variations of the main parameters of the induction motor, like stator and rotor resistances, and the three-phase magnetizing inductance. The proposed controller has been tested both in numerical simulation and experimentally on a suitably designed test setup. Moreover, it has been compared with the classical feedback linearization based on linear controllers, highlighting the improvements in terms of dynamic performance, when parameter variations occur. Results confirm a significant increase of the robustness of the controller against parameter variations. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear observer for induction machine drives based on space-vector dynamic model of induction machine, expressed in state form, which presents the peculiarity of taking into consideration the magnetic saturation of the iron core. This observer is particularly suitable in order to obtain high accuracy in rotor flux estimation, in both amplitude and phase position, during working conditions characterized by varying flux, among which the most important are those during electrical losses minimization. A Lyapunov-based convergence analysis is proposed in order to suitably compute the numerical observer gain guaranteeing the convergence of the estimation error. The proposed nonlinear observer has been tested by means of simulations and experiments carried out on a suitably developed test setup. Its behavior has been compared with that obtained with a standard full-order Luenberger observer that assumes the linearity of the magnetization characteristic. The paper shows the capability of the proposed nonlinear observer to correctly estimate amplitude and phase of the rotor flux under flux varying conditions. Moreover, the proposed observer exhibits a higher accuracy than that obtained with the standard observer, which does not consider the estimated magnetization characteristic. [ABSTRACT FROM PUBLISHER]

This paper presents an input–output feedback linearization (FL) control technique for rotating induction motors, which takes into consideration the magnetic saturation of the iron core. Starting from a new formulation of the dynamic model taking into consideration the magnetic saturation expressed in a space-state form in the rotor-flux-oriented reference frame, the corresponding FL technique has been developed. To this aim, a particular care has been given to the choice of nonlinear functions interpolating the magnetic parameters versus the rotor magnetizing current and the corresponding magnetic characteristic. The proposed FL technique has been tested experimentally on a suitably developed test setup and compared, under the same bandwidths of the speed and flux closed loops, with both the FL technique not taking into consideration the magnetic saturation and with the industrial standard in terms of high performance control of the induction motor: field-oriented control. A further comparison with the unique example present in the scientific literature of the FL control technique including the magnetic saturation has been shown, highlighting consistent improvements achievable by the proposed FL in terms of reduced computational demand and better dynamic performance. [ABSTRACT FROM AUTHOR]

This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned according to a six-order discrete-time model of the IM, affected by system and measurement noises, obtained by applying a first-order Euler discretization to a six-order continuous-time model. Some properties of the discrete-time model have been explored. Among these properties, the observability property is relevant, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous-time model and that estimated by the EKF. The results theoretically achieved have been also validated by means of experimental tests carried out on an IM prototype. [ABSTRACT FROM PUBLISHER]