F. Alonge

Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation

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.

Least squares and genetic algorithms for parameter identification of induction motors

Abstract This paper deals with off-line parameter identification of induction motors by means of least square (LS) techniques and genetic algorithms (GA), using stator voltages, stator currents and velocity as input–output data. For analytical identification by LS algorithms, filtering of experimental data is performed by means of anticausal filters. Two models useful for identification are derived in which the products of acceleration and rotor fluxes, usually neglected, are taken into account. The GA-based identification method consists of the determination of the best parameters which match input–output behaviour of the motor. Both methods are investigated and compared by means of experiments carried out on a 1-kW induction motor.

Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

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.

Nonlinear Modeling of DC/DC Converters Using the Hammerstein's Approach

This paper deals with the modelling of highly nonlinear switching power-electronics converters using black-box identification methods. The duty cycle and the output voltage are chosen, respectively, as the input and the output of the model. A nonlinear Hammerstein-type mathematical model, consisting of a static nonlinearity and a linear time-invariant model, is considered in order to cope with the well-known limitations of the more common small-signal models, i.e. the entity of the variations of the variables around a well-defined steady-state operating point and the incorrect reproduction of the steady-state behavior corresponding to input step variations from the above steady-state operating point. The static nonlinearity of the Hammerstein model is identified from input-output couples measured at steady state for constant inputs. The linear model is identified from input-output data relative to a transient generated by a suitable pseudorandom binary sequence constructed with two input values used to identify the nonlinearity. The identification procedure is, first, illustrated with reference to a boost DC/DC converter using results of simulations carried out in the PSpice environment as true experimental results. Then, the procedure is experimentally applied on a prototype of the above converter. In order to show the utility of the Hammerstein models, a PI controller is tuned for a nominal model. Simulation and experimental results are displayed with the aim of showing the peculiarities of the approach that is followed.

Trajectory tracking of underactuated underwater vehicles

This paper deals with a control strategies for underactuated underwater vehicles whose target is the tracking of a space trajectory. A cascade control strategy is employed which brings to a control law consisting of: 1) a kinematic control law, derived from the vehicle kinematic model, which forces this model to track the reference trajectory; and 2) a dynamic control law which forces the system to track the reference signals given by the kinematic control law. Conditions for asymptotic tracking of the trajectory are given with reference to the standard dynamical model of the above vehicle. An observer of the marine current is also added in order to process the control law. Simulation tests illustrate the proposed approach.

Design and Low-Cost Implementation of an Optimally Robust Reduced-Order Rotor Flux Observer for Induction Motor Control

The aim of this paper is to design and analyze reduced-order observers of the rotor flux of induction motors. The design is carried out in two steps. In the first step, a boundary of the stability region of the observation error is obtained corresponding to a chosen Lyapunov function. In the second step, the boundary is translated into a performance index that is minimized with respect to stator and rotor resistance variations and differences of voltages supplying the motor and those supplying the observer in order to obtain the largest stability region. Implementation of the observer on a low-cost fixed-point digital signal processor using look-up tables is described. Experimental results are shown with reference to a prototype consisting of a simple proportional-integral controller, the proposed observer, and a 2.2-kW induction motor; the implementations of both the controller and the observer are carried out on a DS1104 dSpace microcontroller using the fixed-point option.

A Novel Method of Distance Measurement Based on Pulse Position Modulation and Synchronization of Chaotic Signals Using Ultrasonic Radar Systems

This paper deals with a novel method of transmission and receipt of a signal based on both the property of two chaotic systems generating the same chaotic signal when they are synchronized and the property of pulse position modulation (PPM) to be insensitive to the distortions of the transmission channel. The method is discussed in the context of ultrasonic radar systems, in which the transmitter and receiver, which consist of ultrasonic sensors, are near each other, and the received signal consists of the transmitted signal reflected by an obstacle. A reference sinusoidal signal is superimposed to a chaotic signal generated by a master chaotic system, and the whole signal is modulated according to the PPM method and transmitted by the sensor. The received signal is demodulated, and the demodulated signal forces a slave chaotic system to generate the chaotic signal embedded in it, which allows recovery of the sinusoidal signal by subtracting this chaotic signal from the demodulated echo. The difference of the phases of the reference sinusoidal signal and the recovered sinusoidal signal allows computation of the time of flight of the signal and, consequently, the distance of the radar system from the obstacle. The novel method is illustrated and tested by both simulation and experiments. The interference problem between the considered radar system and other radar systems ( crosstalk) is also addressed, and a solution is proposed to avoid it.

Input–Output Feedback Linearization Control With On-Line MRAS-Based Inductor Resistance Estimation of Linear Induction Motors Including the Dynamic End Effects

This paper proposes the theoretical framework and the consequent application of the input-output feedback linearization (FL) control technique to linear induction motors (LIMs). LIM, additionally to rotating induction motor, presents other strong nonlinearities caused by the dynamic end effects, leading to a space-vector dynamic model with time-varying inductance and resistance terms and a braking force term. This paper, starting from a recently developed dynamic model of the LIM taking into consideration its end effects, defines a FL technique suited for LIMs, since it inherently considers its dynamic end effects. Additionally, it proposes a technique for the on-line estimation of the inductor resistance, based on model reference adaptive system (MRAS) on-line estimator; it has been exploited for adapting on-line the FL control action versus inductor resistance variations leading to undesirable steady-state tracking errors. The stability of the proposed MRAS on-line estimator has been proven theoretically, adopting the Popovs criterion for hyperstability. The proposed approach has been validated experimentally on a suitably developed test setup, under both no load and loaded conditions. It has been compared firstly with the simplest control structure, which is the scalar V/f control, secondly under the same closed-loop bandwidths of the flux and speed systems, with the industrial standard in terms of high-performance control technique, i.e., field-oriented control.

The Use of Accelerometers and Gyroscopes to Estimate Hip and Knee Angles on Gait Analysis

In this paper the performance of a sensor system, which has been developed to estimate hip and knee angles and the beginning of the gait phase, have been investigated. The sensor system consists of accelerometers and gyroscopes. A new algorithm was developed in order to avoid the error accumulation due to the gyroscopes drift and vibrations due to the ground contact at the beginning of the stance phase. The proposed algorithm have been tested and compared to some existing algorithms on over-ground walking trials with a commercial device for assisted gait. The results have shown the good accuracy of the angles estimation, also in high angle rate movement.

Parameter Identification of Linear Induction Motor Model in Extended Range of Operation by Means of Input-Output Data

This paper proposes a technique for the off-line estimation of the electrical parameters of the equivalent circuit of linear induction machines (LIM), taking into consideration the end effects, and focuses on the application of an algorithm based on the minimization of a suitable cost function involving the differences of measured and computed by simulation inductor current components. This method exploits an entire start-up transient of the LIM to estimate all the 4 electrical parameters of the machine (Rs, Ls, σLs, Tr). It proposes also a set of tests to be made to estimate the variation of the magnetic parameters of the LIM versus the magnetizing current as well as the magnetizing curve of the machine. Moreover, a methodology for the estimation of the mechanical parameters of the model is proposed as well. The proposed methodology has been verified experimentally on suitably developed test set-up.