Edgar A. Rivas-Araiza

Adjustable Speed Drive Project for Teaching a Servo Systems Course Laboratory

This paper describes an adjustable speed drive for a three-phase motor, which has been implemented as a design for a servo system laboratory course in an engineering curriculum. The platform is controlled and analyzed in a LabVIEW environment and run on a PC. Theory is introduced in order to show the sensorless algorithms. These are computed by means of speed feedback from speed and flux estimators, which provide tracking of these variables in spite of the presence of an uncertain load torque and changes in the time constant. The novel electronic architecture permits students to use low-cost hardware. The project was divided into several subsystems in order to give students the opportunity to construct a tuning procedure for torque and speed control loops.

A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM)

Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the components harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.

A review of parameter estimators and controllers for induction motors based on artificial neural networks

Induction motors (IMs) are the most used electromechanic machines in industrial applications. Their control has become the subject of many studies since the 70s, and there have been several approaches to achieve high-performance adjustable speed drivers (ASDs). The review presented in this article can support the state of some related researches, since it deals with current state-of-the-art of Artificial Neural Networks (ANNs) oriented to experiments that perform motion control with induction motors. It summarizes many previous works focused on IM and can help the reader to have a starting point to begin their own research on choosing a correct type of Neural Network (NN). The paper provides a list of ANNs used to improve the ASD-control, extending the IM-driver life and achieving proper motor operation, their size and performance. A good match between IM parameter values and the data that the controller needs for the induction machine is imperative. Artificial Intelligence (AI) is a helpful tool to achieve this. The summary will also present an overview of different ANN-based drive approaches.

A survey of hybridisation methods of GNSS and wireless LAN based positioning system

Self-localisation has become a matter of course in our daily life. The emerging market of mobile devices still boosts the demand for seamless, ubiquitous positioning. The repertoire of sensors serviceable for localisation provided by current mobile devices is large; however the main positioning systems used today are based on wireless local area networks (WLAN), cellular networks and certainly global navigation satellite systems (GNSS). Because of the accuracy, the vast deployment and the channel characteristics, researchers have been focused on WLAN based positioning systems (WPS) in particular, to achieve seamless positioning. This paper reviews the latest data fusion approaches to seamless positioning by GNSS and WPS. In accordance to the level of data fusion, several approaches are categorised and briefly presented, differences in performance of these approaches are highlighted and future challenges identified.

Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

Indirect Field Oriented Control of an Induction Motor Sensing DC-link Current

This paper describes an analysis method for achieving control torque and speed with vector control for induction motors. An indirect field-oriented output feedback motor controller is presented; it is suitable for low-cost applications. A current model control is used to sense back electromotive force (back-EMF) by means of an analog to digital converter (ADC); its simulation and filtering are discussed. A Current model is the core of this work, but other system modules are analyzed such the proportional-integrative (PI) controller, the space vector with pulse width modulation (SVPWM), and others. Their implementations on a digital signal processor (DSP) along with the electrical power stage are shown. The cascaded structure of the controller allows performing a constructive tuning procedure for position and speed control loop.

Design and Construction of a Didactic 3-DOF Parallel Links Robot Station with a 1-DOF Gripper

The main objective of the construction of a robot station presented in this article is to allow the students to design andproduce a feasible-to-build mechatronic device using robust, low-cost components. It is a tool for students to gainexperience integrating different mechatronic fields of knowledge, as well as practicing the procedures needed tosuccessfully accomplish their own design. The project starts by describing the target requirements to be achieved bythe prototype robot, these requirements will serve as the guideline for the design and further manufacture and testingof the system. The sub-assemblies of the mechanism are analyzed, main technical areas and their processes arediscussed individually emphasizing the methods and materials used, then results are presented along with somepractical recommendations to extend the scope of the project and improve the performance of the prototype robot. Ithas been especially important to maintain the didactical approach and design the platform with affordable componentsthat can be easily obtained; this is also true for the tools and software used. The article is also intended to introducethe student to industrial design methodology, allowing for different manufacturing processes and robot architectures tobe incorporated for the specific scope of the project and the available tools and facilities.

Long-Range Wireless Mesh Network for Weather Monitoring in Unfriendly Geographic Conditions

In this paper a long-range wireless mesh network system is presented. It consists of three main parts: Remote Terminal Units (RTUs), Base Terminal Units (BTUs) and a Central Server (CS). The RTUs share a wireless network transmitting in the industrial, scientific and medical applications ISM band, which reaches up to 64 Km in a single point-to-point communication. A BTU controls the traffic within the network and has as its main task interconnecting it to a Ku-band satellite link using an embedded microcontroller-based gateway. Collected data is stored in a CS and presented to the final user in a numerical and a graphical form in a web portal.

Review: Morphological multiscale fingerprints from connected transformations

In this paper a multiscale study based on the use of morphological connected transformations is presented. In particular, the morphological opening and closing by reconstruction are used as connected transformations. These transformations have the characteristic of preserving contours without introducing new information, in addition to smoothing the processed image as the size of the structuring element increases. Due to this, both connected filters and the identity are used as primitives to introduce two operators that allow a multiscale treatment to the processed image in accordance with the scale parameter. The multiscale morphological operators are called opening-closing by reconstruction and closing-opening by reconstruction, respectively. In the case of the opening-closing by reconstruction transformation, the propagation of extrema when the structuring element enlarges has an interesting behavior. In addition, the fingerprints of the image given as a function of regional maxima, regional minima and scale parameter are defined. Finally, the proposals of this work are illustrated with some examples.

Dedicated feature descriptor for outdoor augmented reality detection

Stable augmented reality applications consist of an accurate registration supported by a robust tracking module. In outdoor locations, the changing environmental and light conditions compromise this tracking. Reliable descriptors under unsettled conditions are essential for this process. The most used descriptors have this distinctive capacity, but computers and mobile devices process them in a long time frame. This paper investigates a new lightweight environment dedicated descriptor (EDD) trained with a machine-learning algorithm. The descriptor analyzes the scene characteristics with elements that can be computed fast and that have distinctive information about the selected area. The complete descriptor is used for semantic feature extraction with the aid of a trained random forest classifier. The descriptor is compared with the most popular descriptors—with respect to speed, accuracy, and invariance to illumination changes, scale, affine transformation, and rotation—and the results show that it is faster and in most cases equally reliable .