Ingeniería Mecatrónica (Mag.)

Permanent URI for this collectionhttp://98.81.228.127/handle/20.500.12404/1750

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Analysis and implementation of active noise control strategies using Piezo and EAP actuators
(Pontificia Universidad Católica del Perú, 2015-06-09) Calderón Chavarri, Jesús Alan; Tafur Sotelo, Julio César; Sattel, Thomas
Currently noise cancellation, which affects the lives of people and in the workplace is achieved through the active noise reduction. This measure is not expensive as passive or semi active measures also permits adequate air conduction in duct ventilation systems. The system control is achieved through a suitable location of the phase in the cancelling noise signal relative to the signal primary noise. Algorithms have been developed and strategies for active noise reduction and its implementation and experimental testing on duct ventilation. The actives elements used are Piezo Actuators and EAP as speakers; Individual and collective operation of the aforementioned actuators is examined. The work was evaluated as follows: Analysis of previous research on existing algorithms for active noise reduction. Study the strategies of simulation and implementation for active noise control algorithms designed.
Optimal control algorithm design for a prototype of active noise control system
(Pontificia Universidad Católica del Perú, 2017-04-28) Manzano Ramos, Edgar André; Tafur Sotelo, Julio César; Calderón Chavarri, Jesús Alan
High-level noise can represent a serious risk for the health, industrial operations often represent continuous exposure to noise, thus an important trouble to handle. An alternative of solution can be the use of passive mechanisms of noise reductions, nonetheless its application cannot diminish low-frequency noise. Active Noise Control (ANC) is the solution used for low-frequency noise, ANC systems work according to the superposition principle generating a secondary anti-noise signal to reduce both. Nevertheless, the generation of an anti-noise signal with same oppose characteristics of the original noise signal presupposes the utilization of special techniques such as adaptive algorithms. These algorithms involve computational costs. The present research present the optimization of a specific ANC algorithm in the step-size criteria. Delayed Filtered-x LMS (FxLMS) algorithm using an optimal step-size is evaluated in a prototype of ANC system.
Optimal control for a prototype of an active magnetic bearing system
(Pontificia Universidad Católica del Perú, 2017-05-24) Aragón Ayala, Danielo Eduardo; Tafur Sotelo, Julio César; Calderón Chavarri, Jesús Alan
First applications of the electromagnetic suspension principle have been in experimental physics, and suggestions to use this principle for suspending transportation vehicles for high-speed trains go back to 1937. There are various ways of designing magnetic suspensions for a contact free support, the magnetic bearing is just one of them [BCK+09]. Most bearings are used in applications involving rotation. Nowadays, the use of contact bearings solves problems in the consumer products, industrial machinery, or transportation equipment (cars, trucks, bicycles, etc). Bearings allow the transmition of power from a motor to moving parts of a rotating machine [M+92]. For a variety of rotating machines, it would be advantageous to replace the mechanical bearings for magnetic bearings, which rely on magnetic elds to perform the same functions of levitation, centering, and thrust control of the rotating parts as those performed by a mechanical bearing. An advantage of the magnetic bearings (controlled or not) against purely mechanical is that magnetic bearings are contactless [BHP12]. As a consequence these properties allow novel constructions, high speeds with the possibility of active vibration control, operation with no mechanical wear, less maintenance and therefore lower costs. On the other hand, the complexity of the active (controlled) and passive (not controlled) magnetic bearings requires more knowledge from mechanics, electronics and control [LJKA06]. The passive magnetic bearing (PMB) presents low power loss because of the absence of current, lack of active control ability and low damping sti ness [FM01, SH08]. On the other hand, active magnetic bearing (AMB) has better control ability and high sti ness, whereas it su ers from high power loss due to the biased current [JJYX09]. Scientists of the 1930s began investigating active systems using electromagnets for high-speed ultracentrifuges. However, not controlled magnetic bearings are physically unstable and controlled systems only provide proper sti ness and damping through sophisticated controllers and algorithms. This is precisely why, until the last decade, magnetic bearings did not become a practical alternative to rolling element bearings. Today, magnetic bearing technology has become viable because of advances in microprocessing controllers that allow for con dent and robust active control [CJM04]. Magnetic bearings operate contactlessly and are therefore free of lubricant and wear. They are largely immune to heat, cold and aggressive substances and are operational in vacuum. Because of their low energy losses they are suited for applications with high rotation speeds. The forces act through an air gap, which allows magnetic suspension through hermetic encapsulations [Bet00].
Physical parameters identification for a prototype of active magnetic bearing system
(Pontificia Universidad Católica del Perú, 2017-05-12) Perea Fabián, Carlos Antonio; Tafur Sotelo, Julio César; Calderón Chavarri, Jesús Alan
In this thesis the algorithms and strategies for active magnetic bearing should be analysed, implemented and simulated in Matlab as well as experimentally tested in the real-time computation system for a prototype of active magnetic bearing. Develop a general method and algorithm identi cation for active magnetic bearings prototype and get real system parameters that allow generate the equation of state of the system to control its further development. The specific objectives in this Thesis are: Develop a data acquisition system for the AMBs. Analyse the mathematical model of the system from the real system. Conduct experiments of a physical model for data collection. Develop an identification algorithm for the parameters of the real AMBs. Validate system developed by testing the prototype.