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In this work, a novel scheme for velocity and position estimation in a UWB range-based localization system is proposed. The suggested estimation strategy allows to overcome two main problems typically encountered in the localization systems. The first one is that it can be suitable for use in environments where the GPS signal is not present or where it might fail. The second one is that no accelerometer measurements are needed for the localization task. Moreover, to deal with the velocity estimation problem, a suitable Kalman–Bucy filter is designed and it is compared, experimentally, with a particle filter by showing the features of the two algorithms in order to be used in a localization context. Additionally, further experimental tests are carried out on a suitable developed test setup in order to confirm the goodness of the proposed approach. [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 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 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]

This paper proposes a speed observer for linear induction motors (LIMs), which is composed of two parts: 1) a linear Kalman filter (KF) for the online estimation of the inductor currents and induced part flux linkage components; and 2) a speed estimator based on the total least squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, estimated by the KF, and provides as output the LIM linear speed, which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine. The end effects of the LIMs have been considered an uncertainty treated by the KF. The TLS EXIN neuron has been used to compute, in recursive form, the machine linear speed online since it is the only neural network able to solve online, in a recursive form, a TLS problem. The proposed KF TLS speed estimator has been tested experimentally on a suitably developed test setup, and it has been compared with the classic extended KF. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the design of an extended complex Kalman filter (ECKF) for estimating the state of an induction motor (IM) model, and for sensorless control of systems employing this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. The observability analysis of this model is first performed by assuming that a third order ECKF has to be designed, by neglecting the mechanical equation of the IM model, which is a valid hypothesis when the motor is operated at constant rotor speed. It is shown that this analysis is more effective and easier than the one performed on the corresponding real-valued model, as it allows the observability conditions to be directly obtained in terms of stator current and rotor flux complex-valued vectors. Necessary observability conditions are also obtained along with the well-known sufficient ones. It is also shown that the complex-valued implementation allows a reduction of 35% in the computation time w.r.t. the standard real-valued one, which is obtained thanks to the lower dimensions of the matrices of the ECKF w.r.t. the ones of the real-valued implementation and the fact that no matrix inversion is required. The effectiveness of the proposed ECKF is shown by means of simulation in Matlab/Simulink environment and through experiments on a real low-power drive. [Copyright &y& Elsevier]