Active disturbance cancellation in nonlinear dynamical systems using neural networks

A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model

Development of Novel Techniques to Study Nonlinear Active Noise Control

Active noise control has been a field of growing interest over the past few decades. The challenges thrown by active noise control have attracted the notice of the scientific community to engage them in intense level of research. Cancellation of acoustic noise electronically in a simple and efficient way is the vital merit of the active noise control system. A detailed study about existing strategies for active noise control has been undertaken in the present work. This study has given an insight regarding various factors influencing performance of modern active noise control systems. The development of new training algorithms and structures for active noise control are active fields of research which are exploiting the benefits of different signal processing and soft- computing techniques. The nonlinearity contributed by environment and various components of active noise control system greatly affects the ultimate performance of an active noise canceller. This fact motivated to pursue the research work in developing novel architectures and algorithms to address the issues of nonlinear active noise control. One of the primary focus of the work is the application of artificial neural network to effectively combat the problem of active noise control. This is because artificial neural networks are inherently nonlinear processors and possesses capabilities of universal approximation and thus are well suited to exhibit high performance when used in nonlinear active noise control. The present work contributed significantly in designing efficient nonlinear active noise canceller based on neural network platform. Novel neural filtered-x least mean square and neural filtered-e least mean square algorithms are proposed for nonlinear active noise control taking into consideration the nonlinear secondary path. Employing Legendre neural network led the development of a set new adaptive algorithms such as Legendre filtered-x least mean square, Legendre vi filtered-e least mean square, Legendre filtered-x recursive least square and fast Legendre filtered-x least mean square algorithms. The proposed algorithms outperformed the existing standard algorithms for nonlinear active noise control in terms of steady state mean square error with reduced computational complexity. Efficient frequency domain implementation of some the proposed algorithms have been undertaken to exploit its benefits. Exhaustive simulation studies carried out have established the efficacy of the proposed architectures and algorithms

Active Control of Pressure Pulsation in a Switched Inertance Hydraulic System

The nature of digital hydraulic systems may cause pressure pulsation problems. For example, switched inertance hydraulic systems (SIHS), which are applied to adjust or control flow and pressure by a means that does not rely on dissipation of power, have noise problems due to the pulsed nature of the flow. An effective method to reduce the pulsation is important to improve system performance and increase efficiency. Although passive systems to reduce the noise have been shown to be effective in many situations, their attenuation frequency range is limited and they may be bulky. Furthermore, attenuation devices based on expansion chambers, accumulators or hoses are likely to be unsuitable for SIHS as they add compliance to the system and would impair the dynamic response. This thesis is concerned with issues relating to the development of an active noise canceller for attenuating the pressure pulsation which is caused primarily by pulsed flow from high-speed valves in SIHS. Active control methods are widely and successfully applied in the area of structureborne noise (SBN) and air-borne noise (ABN) cancellation. The idea is using the intentional superposition of waves to create a destructive interference pattern such that a reduction of the unwanted noise occurs. However, applications for fluid-borne noise (FBN) attenuation based on the ‘Active noise control (ANC) principle’ are rare due to the restriction of the hardware and experimental apparatus in previous researches. In this thesis, an adaptive controller has been developed for active control of pressure pulsation in hydraulic system. The principle of the adaptive LMS filter and details of the controller design are described and the implementation was carried out through simulation. The designed controller was applied on a vibration test rig initially prior to the hydraulic testing in order to investigate its advantages and limitations in practice. Extensive testing on a switched inertance hydraulic rig proved that the controller, which used a piezoelectric valve with fast response and good bandwidth, is effective and that it has several advantages over previous methods, being effective for low frequency cancellation, with a quick response, and is robust and versatile. A novel method for the accurate measurement of unsteady flowrate in a pipe was proposed. This was applied and validated on a pipe, and was shown to give good results. This method solves the difficulty for measuring the unsteady flowrate currently by using easy-measured signals, such as pressures. It can be used widely for predicting the unsteady flowrate along the pipe.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of novel hybrid method and geometrical configuration-based active noise control system for circular cylinder and slat noise prediction and reduction applications

This thesis presents a study about the application of a geometrical configuration-based feedforward adaptive active noise control (ANC) system in the low-frequency range of flow-induced (aeroacoustics) noise cancellation and the investigation on the effects of different geometrical configurations on the cancellation performance in the presence of the residual noise signal magnitude (in decibel) or the average amount of cancellation (in decibel). The first motivation is that according to the literature review, the passive flow control is limited in the practical consideration and the active flow control performs better than the passive flow control, especially for the low-frequency range. Consider the principle of the active flow control is the same as the ANC technique, therefore, it is feasible to apply the ANC technique in cancelling the low-frequency range of the far-field (aeroacoustics) noise, which provides instructions on the future practical experiments. The second motivation is that we want to explore the effects of different geometrical configurations on the cancellation performance and it provides instructions on the implementation in future practical experiments. To predict the far-field (aeroacoustics) noise, the computational fluid dynamics (CFD) and the Ffowcs Williams and Hawkings (FW-H) equations are used separately for unsteady flow calculation and far-field (aeroacoustics) noise prediction. The proposed ANC system is used for the low-frequency range of the far-field (aeroacoustics) noise cancellation. Soft computing techniques and evolutionary-computing-based techniques are employed as the parameter adjustment mechanism to deal with nonlinearities existed in microphones and loudspeakers. The case study about the landing gear noise cancellation in the two-dimensional computational domain is completed. Simulation results validate the accuracy of the obtained acoustic spectrum with reasonable error because of the mesh resolution and computer capacity. It is observed that the two-dimensional approach can only predict discrete values of sound pressure level (SPL) associated with the fundamental frequency (Strouhal number) and its harmonics. Cancellation results demonstrate the cancellation capability of the proposed ANC system for the low-frequency range of far-field (aeroacoustics) noise and reflect that within the reasonable physical distance range, the cancellation performance will be better when the detector is placed closer to the secondary source in comparison with the primary source. This conclusion is the main innovative contribution of this thesis and it provides useful instructions on future practical experiments, but detailed physical distance values must be dependent on individual cases

Behavioral modeling techniques for power amplifier digital pre-distortion

Abstract. The dramatic increase in the capacity of telecommunication networks has increased the requirements of the devices, one example of which is the continuously widening bandwidth. Using broadband signals requires often high linearity from the transmitter — and especially from the power amplifier at the end of the transmitter chain — but at the same time, it should operate as efficiently as possible. The power amplifier is the most power consuming component in the transmitter and inherently nonlinear, so its linearization is an essential part of the overall system performance. Of the current linearization techniques, digital pre-distortion has established itself as the most common tool for providing better linearity and efficiency in a power amplifier and transmitter. In this thesis, the performance of power amplifier models used in digital pre-distortion was investigated and their differences compared to the more complex reference model used in the actual base station product. The aim of this thesis was to create a behavioral model that corresponds the physical component as accurately as the reference model. This behavioral model could be used for example to design and optimize a power amplifier and linearization algorithm without the need for the secret model for the product. This, in its turn, would result in more efficient work with third parties such as component vendors and reduce the linearization time. The performance parameters of the behavioral models were introduced at the beginning of the thesis. These were also used in later parts to analyse the measurement results. Power amplifier linearization measurements were performed under laboratory conditions on a MATLAB® test bench. From the results, it was found that the use of a simple memoryless behavioral model is not enough to describe the physical nonlinear component with sufficient accuracy. The results also showed that the complexity of the model reduces its accuracy if the model coefficients are not correctly positioned. In this thesis, we succeeded in creating a memory model that describes the reference model sufficiently accurately on several meters, taking into account also the memory effects of the power amplifier. This thesis thus provides a good basis for further development of the actual modeling tool for product projects.Tehovahvistimen käyttäytymistason mallinnustekniikat esisärötyksen tueksi. Tiivistelmä. Tietoliikenneverkkojen kapasiteetin räjähdysmäinen kasvu on lisännyt laitteiden vaatimuksia, joista yhtenä esimerkkinä on jatkuvasti suureneva kaistanleveys. Laajakaistaiset signaalit vaativat usein lähettimeltä — ja etenkin lähettimen loppupäässä olevalta tehovahvistimelta — korkeaa lineaarisuutta, mutta samalla sen on toimittava mahdollisimman tehokkaasti. Tehovahvistin on lähettimen eniten tehoa kuluttava komponentti ja luonnostaan epälineaarinen, joten sen linearisointi on oleellinen osa koko systeemin suorituskykyä. Nykyisistä linearisointitekniikoista digitaalinen esisärötys on vakiinnuttanut paikkansa yleisimpänä työkaluna paremman lineaarisuuden ja tehokkuuden saavuttamiseksi tehovahvistimessa. Tässä diplomityössä tutkittiin digitaalisessa esisärötyksessä käytettävien käyttäytymistason tehovahvistinmallien suorituskykyjä ja niiden eroja varsinaisessa tukiasematuotteessa käytettävään, monimutkaisempaan referenssimalliin verrattuna. Työn tavoitteena oli luoda käyttäytymismalli, jolla voidaan kuvata fyysistä komponenttia yhtä tarkasti kuin referenssimallilla. Mallia voitaisiin käyttää esimerkiksi uuden tehovahvistimen suunnittelussa ilman, että kaupalliseen tuotteeseen tulevaa, salaista referenssimallia on tarve käyttää. Näin voitaisiin tehostaa työskentelyä ulkopuolisten tahojen, kuten komponenttitoimittajien kanssa ja vähentää linearisointiin käytettävää aikaa. Työn alussa esiteltiin käyttäytymismallien suorituskykyparametrit, joita käytettiin mittaustulosten analysointiin. Tehovahvistimien linearisointimittaukset suoritettiin laboratorio-olosuhteissa MATLAB®-testipenkissä. Mittauksissa todettiin, että yksinkertainen, muistiton käyttäytymismalli ei riitä kuvaamaan fyysistä komponenttia riittävän tarkasti. Tuloksista pääteltiin myös, että liian kompleksinen malli heikentää sen tarkkuutta. Työssä onnistuttiin luomaan muistillinen käyttäytymismalli, joka kuvaa referenssimallia riittävän tarkasti usealla eri mittarilla tarkastellen — huomioiden osaltaan myös tehovahvistimen muistiefektejä. Tämä opinnäytetyö tarjoaa siis hyvän pohjan varsinaisen mallinnustyökalun jatkokehitykselle tuoteprojekteihin

Advances In Internal Model Principle Control Theory

In this thesis, two advanced implementations of the internal model principle (IMP) are presented. The first is the identification of exponentially damped sinusoidal (EDS) signals with unknown parameters which are widely used to model audio signals. This application is developed in discrete time as a signal processing problem. An IMP based adaptive algorithm is developed for estimating two EDS parameters, the damping factor and frequency. The stability and convergence of this adaptive algorithm is analyzed based on a discrete time two time scale averaging theory. Simulation results demonstrate the identification performance of the proposed algorithm and verify its stability. The second advanced implementation of the IMP control theory is the rejection of disturbances consisting of both predictable and unpredictable components. An IMP controller is used for rejecting predictable disturbances. But the phase lag introduced by the IMP controller limits the rejection capability of the wideband disturbance controller, which is used for attenuating unpredictable disturbance, such as white noise. A combination of open and closed-loop control strategy is presented. In the closed-loop mode, both controllers are active. Once the tracking error is insignificant, the input to the IMP controller is disconnected while its output control action is maintained. In the open loop mode, the wideband disturbance controller is made more aggressive for attenuating white noise. Depending on the level of the tracking error, the input to the IMP controller is connected intermittently. Thus the system switches between open and closed-loop modes. A state feedback controller is designed as the wideband disturbance controller in this application. Two types of predictable disturbances are considered, constant and periodic. For a constant disturbance, an integral controller, the simplest IMP controller, is used. For a periodic disturbance with unknown frequencies, adaptive IMP controllers are used to estimate the frequencies before cancelling the disturbances. An extended multiple Lyapunov functions (MLF) theorem is developed for the stability analysis of this intermittent control strategy. Simulation results justify the optimal rejection performance of this switched control by comparing with two other traditional controllers

Signal Processing Research Program

Contains table of contents for Part III, table of contents for Section 1, an introduction and reports on fourteen research projects.Charles S. Draper Laboratory Contract DL-H-404158U.S. Navy - Office of Naval Research Grant N00014-89-J-1489National Science Foundation Grant MIP 87-14969Battelle LaboratoriesTel-Aviv University, Department of Electronic SystemsU.S. Army Research Office Contract DAAL03-86-D-0001The Federative Republic of Brazil ScholarshipSanders Associates, Inc.Bell Northern Research, Ltd.Amoco Foundation FellowshipGeneral Electric FellowshipNational Science Foundation FellowshipU.S. Air Force - Office of Scientific Research FellowshipU.S. Navy - Office of Naval Research Grant N00014-85-K-0272Natural Science and Engineering Research Council of Canada - Science and Technology Scholarshi