J. A. Dente

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.

Using immunology principles for fault detection

The immune system is a cognitive system of complexity comparable to the brain and its computational algorithms suggest new solutions to engineering problems or new ways of looking at these problems. Using immunological principles, a two- (or three-) module algorithm is developed which is capable of launching a specific response to an anomalous situation for diagnostic purposes. Experimental results concerning fault detection in an induction motor are presented as an example illustrating how the immune-based system operates, discussing its capabilities, drawbacks, and future developments.

Torque ripple minimization in a switched reluctance drive by neuro-fuzzy compensation

A simple power electronic drive circuit and fault tolerance of converter are specific advantages of SRM drives, but excessive torque ripple has limited their use to special applications. It is well known that controlling the current shape adequately can minimize the torque ripple. This paper presents a new method for shaping the motor currents to minimize the torque ripple, using a neuro-fuzzy compensator. In the proposed method, a compensating signal is added to the output of a PI controller, in a current-regulated speed control loop. Numerical results are presented in this paper, with an analysis of the effects of changing the form of the membership function of the neuro-fuzzy compensator.

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.

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.

Language identification of controlled systems: modeling, control, and anomaly detection

Formal language techniques have been used in the past to study autonomous dynamical systems. However, for controlled systems, new features are needed to distinguish between information generated by the system and input control. We show how the modeling framework for controlled dynamical systems leads naturally to a formulation in terms of context-dependent grammars. A learning algorithm is proposed for online generation of the grammar productions, this formulation then being used for modeling, control and anomaly detection. Practical applications are described for electromechanical drives. Grammatical interpolation techniques yield accurate results, and the pattern detection capabilities of the language-based formulation makes it a promising technique for the early detection of anomalies or faulty behavior.

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.

On using fuzzy logic to integrate learning mechanisms in an electro-hydraulic system. I. Actuator's fuzzy modeling

Drive systems are usually modeled using a mathematical characterization of their physical phenomena. However, the difficulty in establishing a relevant model representation, in particular for electro-hydraulic systems, makes important the search for other modeling mechanisms that allow the combination of previously compiled systems knowledge with acquired experimental information. This paper, divided into two parts, describes the potential and possible drawbacks of integrating fuzzy learning mechanisms into a drive system that includes an electro-hydraulic actuator. First, experimental verification of the actuators fuzzy modeling is presented in Part I of the paper, where the variable selection problem and the performance of the learning algorithm are discussed. In Part II, extensive experimental results employing the extracted fuzzy model and associated learning algorithm are presented. The feasibility and effectiveness of integrating fuzzy learning mechanisms into the actuators control is also discussed.

Electric bicycle using batteries and supercapacitors

In this paper, a traction system useful for an autonomous Electric Vehicle of individual use is described. The developed system is constituted in a first approach by two different power sources: one is constituted by batteries or by fuel cells, and the other by supercapacitors. This paper describes a technical solution joining and accomplishing the usage of two energy storage systems in the same traction system. In the developed system, the supercapacitors run as element that store energy temporarily and that can be used to retrieve energy. Starting from the functional characteristics of typical electrical vehicles and characterization of a typical routing profile, the energy consumption is obtained. In order to characterize and design the system, this is described in detail, namely the supercapacitors models, the battery, the power converters and the implemented strategy of control. According to the obtained results, a control strategy that allows an effective management of the stored energy in the system regarding the vehicles optimal functioning and increasing its autonomy is also presented and discussed. Based on experimental and simulation results, the advantages and disadvantages of the proposed solution are presented.