Filippo D'Ippolito

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.

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.

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.

Identification and Robust Control of DC/DC Converter Hammerstein Model

This paper deals with model-based robust control of DC/DC power electronic converters. The converter is described by means of a Hammerstein model consisting of the nonlinear static characteristics of the converter and a linear time-invariant (LTI) uncertain model whose parameters depend on the actual duty-cycle operating range. This suggests that the controller be designed using robust control techniques. In view of applying robust control, identification of the earlier LTI models is performed by means of simulation experiments, carried out on a converter switching model implemented on MATLAB/SIMULINK environment. Internal model control (IMC) structure is employed for the controller design, but its implementation is performed using the equivalent feedback control structure. Comparison with some controllers designed starting from models that do not require identification steps is performed with the aim of showing the advantages connected to the availability of a suitable model, which describes the essential aspects of the behavior of the system, for control purposes. Comparison with a PI controller designed by means of phase margin assignment is carried out with the aim of justifying the use of more sophisticated control methods. Experimental results are also shown that aimed to prove the validity of the whole approach. Comparison of experimental and simulation results is also performed.

Robustness analysis of an Extended Kalman Filter for sensorless control of induction motors

This paper deals with robustness analysis of Extended Kalman Filters (EKFs) for sensorless motion control of induction motors. Analysis is carried out by means of simulation experiments considering a conventional EKF, in which system and measurement noise covariance matrices are constant, and an adaptive EKF in which the system noise covariance matrix is updated on-line using a PID-type algorithm driven by the stator current estimation errors.

Descriptor-Type Kalman Filter and TLS EXIN Speed Estimate for Sensorless Control of a Linear Induction Motor

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.