Arturo Molina

By Daniel R. Ramirez Rebollo, Pedro Ponce Cruz and Arturo Molina

This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems. The main advantage of this algorithm named CODA (Cognition from Data). Is; it can learn from limited data samples- that is given a single example and the algorithm will create its own knowledge. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. The algorithm is explained with pseudocode, flowcharts and block diagrams. The clonal/mutation results are presented with a simple example. It can be seen graphically how new data that has a completely new probability distribution. Finally, the first application where CODA is used, a humanoid hand is presented. In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation.

Part of the book: Recent Advances in Robotic Systems

By Ivan Villanueva, Antonio Rosales, Pedro Ponce and Arturo Molina

Doubly-fed induction generator (DFIG) is the most implemented electric machine in wind energy conversion systems (WECSs) due to reduced size converter, active and reactive power control, and economic factors. However, the power electronic stage needs an accurate controller that allows to follow the stator power regulation despite the presence of disturbances. On the other hand, sliding-mode control (SMC) offers a fast-dynamic response and provides insensitivity to matched and bounded disturbance/uncertainties, and its natural discontinuous control signals can be used for direct switching of power electronic devices. Switching frequency must be maintained inside acceptable values to avoid exceeding the maximum admissible switching frequency of semiconductors. The contribution of this chapter is a stator-flux–oriented SMC with a hysteresis band that limits the switching frequency of power electronic devices to a set value. Furthermore, the proposed SMCs ensure robustness against bounded low-voltage grid faults. Unlike other nonmodulated techniques like direct torque control (DTC), there is no necessity of modifying the controller structure for withstanding low-depth voltage dips. The controller injects negative sequence voltage/currents to compensate the unbalanced conditions. The advantages of the proposed SMC control are validated via simulations.

Part of the book: Adaptive Robust Control Systems

By Pedro Ponce, Luis Arturo Soriano, Arturo Molina and Manuel Garcia

In recent years, brushless direct current motor (BLDCM) applications have been increased due to their advantages as low size, mechanical torque, high-speed range, to mention some. The BLDCM control is required to operate at high frequency, high temperature, large voltage, and quick changes of current; as a result of this kind of operation, the power drive lifetime is affected. The power drives commonly utilized insulated gate bipolar transistors (IGBTs) and metal oxide semiconductor field effect transistors (MOSFETs), which present power losses, on-state losses, and switching losses caused by temperature oscillations. Hence, the power losses are related to the command signals generated by the controller. In this sense, the BLDC motor drive controller design, frequently, does not take into account the power losses and the temperature oscillations, which cause the IGBT lifetime decrease or premature fail. In this chapter, a brushless DC motor drive is designed based on a fuzzy controller tuned with the particle swarm optimization algorithm, where the temperature oscillations and speed set points are considered in order to increase IGBT module lifetime. The validation of the proposed fuzzy-PSO controller is carried out by the co-simulation between LabVIEW™ and Multisim™ and finally analysis and conclusion of the work.

Part of the book: Electric Machines for Smart Grids Applications