REAL TIME FUZZY CONTROLLER FOR QUADROTOR STABILITY CONTROL

In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM CortexM4 based controller board. We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.

Fully Connected PLL Networks: How Filter Determines the Number of Nodes

Synchronization plays an important role in telecommunication systems, integrated circuits, and automation systems. Formerly, the masterslave synchronization strategy was used in the great majority of cases due to its reliability and simplicity. Recently, with the wireless networks development, and with the increase of the operation frequency of integrated circuits, the decentralized clock distribution strategies are gaining importance. Consequently, fully connected clock distribution systems with nodes composed of phase-locked loops (PLLs) appear as a convenient engineering solution. In this work, the stability of the synchronous state of these networks is studied in two relevant situations: when the node filters are first-order lag-lead low-pass or when the node filters are second-order low-pass. For first-order filters, the synchronous state of the network shows to be stable for any number of nodes. For second-order filter, there is a superior limit for the number of nodes, depending on the PLL parameters. Copyright (C) 2009 Atila Madureira Bueno et al.

Complementary filter design with three frequency bands: robot attitude estimation

This paper extents the by now classic sensor fusion complementary filter (CF) design, involving two sensors, to the case where three sensors that provide measurements in different bands are available. This paper shows that the use of classical CF techniques to tackle a generic three sensors fusion problem, based solely on their frequency domain characteristics, leads to a minimal realization, stable, sub-optimal solution, denoted as Complementary Filters3 (CF3). Then, a new approach for the estimation problem at hand is used, based on optimal linear Kalman filtering techniques. Moreover, the solution is shown to preserve the complementary property, i.e. the sum of the three transfer functions of the respective sensors add up to one, both in continuous and discrete time domains. This new class of filters are denoted as Complementary Kalman Filters3 (CKF3). The attitude estimation of a mobile robot is addressed, based on data from a rate gyroscope, a digital compass, and odometry. The experimental results obtained are reported.

High-Resolution Mass Spectrometry applied to the identification of transformation products of quinolones from stability studies and new metabolites of enrofloxacin in chicken muscle tissues

The aim of this work was the identification of new metabolites and transformation products (TPs) in chicken muscle from Enrofloxacin (ENR), Ciprofloxacin (CIP), Difloxacin (DIF) and Sarafloxacin (SAR), which are antibiotics that belong to the fluoroquinolones family. The stability of ENR, CIP, DIF and SAR standard solutions versus pH degradation process (from pH 1.5 to 8.0, simulating the pH since the drug is administered until its excretion) and freeze-thawing (F/T) cycles was tested. In addition, chicken muscle samples from medicated animals with ENR were analyzed in order to identify new metabolites and TPs. The identification of the different metabolites and TPs was accomplished by comparison of mass spectral data from samples and blanks, using liquid chromatography coupled to quadrupole time-of-flight (LC-QqToF) and Multiple Mass Defect Filter (MMDF) technique as a pre-filter to remove most of the background noise and endogenous components. Confirmation and structure elucidation was performed by liquid chromatography coupled to linear ion trap quadrupole Orbitrap (LC-LTQ-Orbitrap), due to its mass accuracy and MS/MS capacity for elemental composition determination. As a result, 21 TPs from ENR, 6 TPs from CIP, 14 TPs from DIF and 12 TPs from SAR were identified due to the pH shock and F/T cycles. On the other hand, 14 metabolites were identified from the medicated chicken muscle samples. Formation of CIP and SAR, from ENR and DIF, respectively, and the formation of desethylene-quinolone were the most remarkable identified compounds.

High-resolution mass spectrometry applied to theidentification of transformation products of quinolones from stability studies and new metabolites of enrofloxacin in chicken muscle tissues

The aim of this work was the identification of new metabolites and transformation products (TPs) in chicken muscle from Enrofloxacin (ENR), Ciprofloxacin (CIP), Difloxacin (DIF) and Sarafloxacin (SAR), which are antibiotics that belong to the fluoroquinolones family. The stability of ENR, CIP, DIF and SAR standard solutions versus pH degradation process (from pH 1.5 to 8.0, simulating the pH since the drug is administered until its excretion) and freeze-thawing (F/T) cycles was tested. In addition, chicken muscle samples from medicated animals with ENR were analyzed in order to identify new metabolites and TPs. The identification of the different metabolites and TPs was accomplished by comparison of mass spectral data from samples and blanks, using liquid chromatography coupled to quadrupole time-of-flight (LC-QqToF) and Multiple Mass Defect Filter (MMDF) technique as a pre-filter to remove most of the background noise and endogenous components. Confirmation and structure elucidation was performed by liquid chromatography coupled to linear ion trap quadrupole Orbitrap (LC-LTQ-Orbitrap), due to its mass accuracy and MS/MS capacity for elemental composition determination. As a result, 21 TPs from ENR, 6 TPs from CIP, 14 TPs from DIF and 12 TPs from SAR were identified due to the pH shock and F/T cycles. On the other hand, 14 metabolites were identified from the medicated chicken muscle samples. Formation of CIP and SAR, from ENR and DIF, respectively, and the formation of desethylene-quinolone were the most remarkable identified compounds.

Estudi comparatiu dels filtres de Kalman i IMM per a navegació

Els sistemes híbrids de navegació integren mesures de posició i velocitat provinents de satèl·lits (GPS) i d’unitats de mesura inercials (IMU).Les dades d’aquests sensors s’han de fusionar i suavitzar, i per a aquest propòsit existeixen diversos algorismes de filtratge, que tracten les dades conjuntament o per separat. En aquest treball s’han codificat en Matlab els algorismes dels filtres de Kalman i IMM, i s’han comparat les seves prestacions en diverses trajectòries d’un vehicle. S’han avaluat quantitativament els errors dels dos filtres, i s’han sintonitzat els seus paràmetres per a minimitzar aquests errors. Amb una correcta sintonia dels filtres, s’ha comprovat que el filtre IMM és superior al filtre de Kalman, tant per maniobres brusques com per maniobres suaus, malgrat que la complexitat i el temps de càlcul requerit són majors.

Hybrid du/dt-filter in frequency converter applications

The aim of the thesis is to investigate the hybrid LC filter behavior in modern power drives; to analyze the influence of such a du/dt filter on the control system stability. With the implementation of the inverter output RLC filter the motor control becomes more complicated. And during the design process the influence of the filter on the motor should be considered and the filter RLC parameters should be constrained.

Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown non-linear processes

This paper presents a controller design scheme for a priori unknown non-linear dynamical processes that are identified via an operating point neurofuzzy system from process data. Based on a neurofuzzy design and model construction algorithm (NeuDec) for a non-linear dynamical process, a neurofuzzy state-space model of controllable form is initially constructed. The control scheme based on closed-loop pole assignment is then utilized to ensure the time invariance and linearization of the state equations so that the system stability can be guaranteed under some mild assumptions, even in the presence of modelling error. The proposed approach requires a known state vector for the application of pole assignment state feedback. For this purpose, a generalized Kalman filtering algorithm with coloured noise is developed on the basis of the neurofuzzy state-space model to obtain an optimal state vector estimation. The derived controller is applied in typical output tracking problems by minimizing the tracking error. Simulation examples are included to demonstrate the operation and effectiveness of the new approach.

The composite-tendency RAW filter in semi-implicit integrations

The time discretization in weather and climate models introduces truncation errors that limit the accuracy of the simulations. Recent work has yielded a method for reducing the amplitude errors in leapfrog integrations from first-order to fifth-order. This improvement is achieved by replacing the Robert--Asselin filter with the RAW filter and using a linear combination of the unfiltered and filtered states to compute the tendency term. The purpose of the present paper is to apply the composite-tendency RAW-filtered leapfrog scheme to semi-implicit integrations. A theoretical analysis shows that the stability and accuracy are unaffected by the introduction of the implicitly treated mode. The scheme is tested in semi-implicit numerical integrations in both a simple nonlinear stiff system and a medium-complexity atmospheric general circulation model, and yields substantial improvements in both cases. We conclude that the composite-tendency RAW-filtered leapfrog scheme is suitable for use in semi-implicit integrations.

The composite-tendency Robert-Asselin-Williams (RAW) filter in semi-implicit integrations

Timediscretization in weatherandclimate modelsintroduces truncation errors that limit the accuracy of the simulations. Recent work has yielded a method for reducing the amplitude errors in leap-frog integrations from first-order to fifth-order.This improvement is achieved by replacing the Robert–Asselin filter with the Robert–Asselin–Williams (RAW) filter and using a linear combination of unfiltered and filtered states to compute the tendency term. The purpose of the present article is to apply the composite-tendency RAW-filtered leapfrog scheme to semi-implicit integrations. A theoretical analysis shows that the stability and accuracy are unaffected by the introduction of the implicitly treated mode. The scheme is tested in semi-implicit numerical integrations in both a simple nonlinear stiff system and a medium-complexity atmospheric general circulation model and yields substantial improvements in both cases. We conclude that the composite-tendency RAW-filtered leap-frog scheme is suitable for use in semi-implicit integrations.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.

The Diffusion Kernel Filter

A particle filter method is presented for the discrete-time filtering problem with nonlinear ItA ` stochastic ordinary differential equations (SODE) with additive noise supposed to be analytically integrable as a function of the underlying vector-Wiener process and time. The Diffusion Kernel Filter is arrived at by a parametrization of small noise-driven state fluctuations within branches of prediction and a local use of this parametrization in the Bootstrap Filter. The method applies for small noise and short prediction steps. With explicit numerical integrators, the operations count in the Diffusion Kernel Filter is shown to be smaller than in the Bootstrap Filter whenever the initial state for the prediction step has sufficiently few moments. The established parametrization is a dual-formula for the analysis of sensitivity to gaussian-initial perturbations and the analysis of sensitivity to noise-perturbations, in deterministic models, showing in particular how the stability of a deterministic dynamics is modeled by noise on short times and how the diffusion matrix of an SODE should be modeled (i.e. defined) for a gaussian-initial deterministic problem to be cast into an SODE problem. From it, a novel definition of prediction may be proposed that coincides with the deterministic path within the branch of prediction whose information entropy at the end of the prediction step is closest to the average information entropy over all branches. Tests are made with the Lorenz-63 equations, showing good results both for the filter and the definition of prediction.

Frequency synthesizer using a hybrid analog/digital loop filter: A low complexity approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Long-term histopathologic evaluation of inferior vena cava after modified Greenfield filter implantation - Experimental study in sheep

Aim. The authors assess a modified Greenfield filter (GF) for the long-term patency, filter tilting and histopathologic alterations of the inferior vena cava (IVC).Methods. Adult sheep (n=7) underwent modified GF placement in the IVC. Cavograms were obtained every 3 months and pulmonary angiography at 12 months. Histopathologic and scanning electron microscopy (SEM) analyses were performed on the IVC explanted at 12 months.Results. Cavograms showed that all IVC were patent at the end of the study. Filter tilting occurred in 2/7 animals and extrusion of struts was not observed. Macroscopic examination at explantation showed minimal venous wall thickening. Microscopic examination showed minimal IVC fibrosis and intimal hyperplasia. SEM showed endothelium on the IVC surface at the filter implantation site and a presumed endothelial layer covering partially or totally the struts. The interface filter-IVC was covered by deposits of leucocytes and platelets. No signs of pulmonary embolism were found in all pulmonary angiograms of both groups.Conclusion. The modified filter presented good biocompatibility, stability and absence of thrombogenicity at 12 months. It presented low tendency to tilting and extrusion of struts. The long-term histopathologic alterations in vena caval wall were minimal and the appearance of the studied filters in the IVC was similar to stents placed in the arterial system.

A linearly tunable low-voltage CMOS transconductor with improved common-mode stability and its application to (gm)-C filters

A linearly tunable low-voltage CMOS transconductor featuring a new adaptative-bias mechanism that considerably improves the stability of the processed-signal common,mode voltage over the tuning range, critical for very-low voltage applications, is introduced. It embeds a feedback loop that holds input devices on triode region while boosting the output resistance. Analysis of the integrator frequency response gives an insight into the location of secondary poles and zeros as function of design parameters. A third-order low-pass Cauer filter employing the proposed transconductor was designed and integrated on a 0.8-mum n-well CMOS standard process. For a 1.8-V supply, filter characterization revealed f(p) = 0.93 MHz, f(s) = 1.82 MHz, A(min) = 44.08, dB, and A(max) = 0.64 dB at nominal tuning. Mined by a de voltage V-TUNE, the filter bandwidth was linearly adjusted at a rate of 11.48 kHz/mV over nearly one frequency decade. A maximum 13-mV deviation on the common-mode voltage at the filter output was measured over the interval 25 mV less than or equal to V-TUNE less than or equal to 200 mV. For V-out = 300 mV(pp) and V-TUNE = 100 mV, THD was -55.4 dB. Noise spectral density was 0.84 muV/Hz(1/2) @1 kHz and S/N = 41 dB @ V-out = 300 mV(pp) and 1-MHz bandwidth. Idle power consumption was 1.73 mW @V-TUNE = 100 mV. A tradeoff between dynamic range, bandwidth, power consumption, and chip area has then been achieved.