P.J. Costa Branco

Simulation of a 6/4 switched reluctance motor based on Matlab/Simulink environment

A Matlab/Simulink environment to simulate a 6/4-switched reluctance motor is described. From its linear model to the nonlinear model, its dynamics is described and discussed in detail. All simulations are completely documented by their block diagrams and corresponding special Matlab functions and parameters quickly develop its model to the reader. Based on the developed model, simulation studies are performed and compared with measured motor phase currents either for hysteresis and voltage control strategies, and the steady-state motor operation to validate the model.

Derivation of a continuum model and its electric equivalent-circuit representation for ionic polymer–metal composite (IPMC) electromechanics

Biomedical engineering applications of ionic polymer–metal composites such as motion devices for endoscopy, pumps, valves, catheter navigation mechanisms and spinal pressure sensors make it important to properly model IPMCs for engineering design. In particular, IPMC continuum models and their electric equivalent circuit representation are critical to a more efficient design of IPMC devices. In this paper, we propose a new continuum electromechanical model to understand and predict the electrical/mechanical behavior of the IPMC. An IPMC lumped-parameter circuit is derived from its continuum model to predict the relationship between its voltage and current signals. Although based on previous works of Shahinpoor and Nemat-Nasser, our model was derived on a macroscopic level, the water effects were assumed negligible when compared with the electrical effects of mobile ions for the IPMC motion, the model parameters were clearly identified in their physical meaning, and an equivalent-circuit IPMC model was determined from the established continuum electromechanical model. Experiments are done with two IPMC pieces having different dimensions, which were previously immersed in a sodium solution. The IPMCs are current driven, the transverse displacement and voltage signals being measured for different current values, avoiding the water electrolysis phenomenon. Simulations using the analytic models derived are compared with the experimental results and they are found to predict the electrical and mechanical relations very accurately.

Obtaining the magnetic characteristics of an 8/6-switched reluctance machine: FEM analysis and experimental tests

This paper describes the step-by-step procedure for modeling an 8/6 switched reluctance machine. Starting from establishing the geometry of the machine, a finite element modeling approach is initially developed to compute the flux linkage/current/rotor position relationship extracting proper and mutual inductances, also its torque and field distribution characteristics. Experimental tests are performed to obtain the magnetic characteristics of the machine, comparing, correcting, and discussing the results obtained with those of FEM analysis.

Fuzzy logic torque ripple reduction by turn-off angle compensation for switched reluctance motors

A fuzzy-logic-based turn-off angle compensator for torque ripple reduction in a switched reluctance motor is proposed. The turn-off angle, as a complex function of motor speed and current, is automatically changed for a wide motor speed range to reduce torque ripple. Experimental results are presented that show ripple reduction when the turn-off angle compensator is used.

Proposition of an offline learning current modulation for torque-ripple reduction in switched reluctance motors: design and experimental evaluation

A new offline current modulation using a neuro-fuzzy compensation scheme for torque-ripple reduction in switched reluctance motors is presented. The main advantage of the proposed technique is that the torque signal is unnecessary. The compensating signal is learned prior to normal operation in a self-commissioning run, capturing the necessary current shape to reduce the torque ripple. Simulation results verify first the effects of speed and then load changes on the compensator performance. Implementation of the proposed technique in a laboratory prototype shows the feasibility and accuracy of the respective offline scheme.

An experiment in automatic modeling an electrical drive system using fuzzy logic

Electrical drives are usually modeled using circuit theory, with currents or linking fluxes chosen as state variables for its electrical part and rotor speed or position chosen for its mechanical part. Often, its internal structure contains nonlinear relations which are difficult to model such as dead-time, hysteresis, and saturation effects. On the contrary, if the available model is accurate enough, its parameter values are generally difficult to obtain and/or be estimated in real time. Therefore, the paper investigates the use of fuzzy logic for the automatic modeling of electrical drive systems. An experimental system composed of a DC motor supplied from a DC-DC converter is used. The authors underline the unsupervised learning characteristics of the fuzzy algorithm, its memory and generalization capabilities. Some learning situations with critical effects on model performance are presented and discussed, pointing out some results and conclusions concerning the fuzzy modeling process in practice.

Development and Experimental Tests of a Simple Neurofuzzy Learning Sensorless Approach for Switched Reluctance Motors

Despite becoming competitive with ac and dc machines, the necessity for a shaft position transducer makes switched reluctance (SR) machines lose their low cost advantage, mainly as low power machines such as fans and pumps. Many techniques have been proposed for indirect rotor position detection for SR machines. However, their characteristics can be summed up as being based on a lookup table plus an interpolation algorithm, making them specific to a particular machine. For economic reasons and also dynamic performance, sensorless algorithms need a learning mechanism to allow them to adapt to a new SR machine or even adapt to changes in the SRM parameters. This paper presents a novel methodology for position sensor elimination for SR machines. Using the voltage from each conducting phase and the reference current signal as inputs, the rotor speed is first obtained as the output of a neurofuzzy learning system used as a “virtual” speed sensor. Then, the rotor position is determined by integrating the estimated value of speed. The effectiveness of the proposed sensorless technique was investigated through a series of real-time experiments on an SR drive system. The experimental results show that the suggested “virtual” speed sensor and corresponding rotor position can operate well in a sensorless SR speed control system.

On the electromechanics of a piezoelectric transducer using a bimorph cantilever undergoing asymmetric sensing and actuation

This paper presents an analytical, numerical and experimental study of an asymmetric piezoelectric actuator/sensor cantilever beam. The structure consists in a three-layered laminate with a piezoceramic acting as actuator, an elastic material layer and a second piezoceramic layer that can operate as a sensor or actuator. The coupled expansion-bending motion of the system is analytically resolved, where the governing electromechanical expansion and bending motion equations are obtained. Explicitly analytic solutions for longitudinal and transverse displacements, and also the mechanical/electrical frequency response of the structure are calculated. A finite element model (FEM) is developed and used to evaluate the accuracy of the analytic model. Experimental results are used to verify the frequency response of the structure, and validate the theoretical and FEM models.

Fuzzy systems modeling in practice

Abstract Instead of describing a fuzzy modeling algorithm that is new, powerful, robust, and with outstanding learning abilities, the objective of this paper is to point out four important topics usually “ignored” in fuzzy models design. These are: 1. The generalization ability of the fuzzy model. 2. The appearance of empty rules at a fuzzy model whose conclusions could not be extracted. 3. The presence of noise as source of ambiguity to the fuzzy model. 4. The influence of training set size on learning performance. These topics are analyzed and discussed by modeling a linear functional relation using a basic learning algorithm. These conditions allow a better understanding, visualization, and separation of the causes affecting the fuzzy models performance when using this learning algorithm. Results show that it is important to understand what information can be obtained from a previous analysis of the training data which can help to design reasonable and efficient fuzzy models to work in practical environments.

A fuzzy relational identification algorithm and its application to predict the behaviour of a motor drive system

Fuzzy relational identification builds a relational model describing a system’s behaviour by a nonlinear mapping between its variables. In this paper, we propose a new fuzzy relational algorithm based on the simplified max–min relational equation. The algorithm presents an adaptation method applied to the gravity-centre of each fuzzy set based on the error integral value between the measured and predicted system’s output, and uses the concept of time-variant universe of discourse. The identification algorithm also includes a method to attenuate the noise influence in the extracted system’s relational model using a fuzzy filtering mechanism. The algorithm is applied to a one-step forward prediction of a simulated and experimental motor drive system. The identified model has its input–output variables (stator-reference current and motor speed signal) treated as fuzzy sets, whereas the relations existing between them are described by means of a matrix R defining the relational model extracted by the algorithm. The results show the good potentialities of the algorithm in predicting the behaviour of the system and in attenuating through the fuzzy filtering method possible noise distortions in the relational model.