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Las enfermedades cardiovasculares (ECV) son la primera causa de muerte de las enfermedades no transmisibles en el mundo, figurando las dislipidemias como una irregularidad frecuente y principal factor de riesgo. El gen SCARB1 codifica el receptor SR-B1, el cual participa en la depuración del colesterol y captación selectiva de HDL. La sobre expresión del SR-B1 disminuye el HDL-C y la aterosclerosis, mientras que su ausencia incrementa el HDL-C y eleva la aterosclerosis. Objetivo. Identificar el tipo de asociación de las dislipidemias con el SNP rs5888 del gen SRB1 (exón 8) en población aparentemente sana del estado de Morelos México. Metodología. De un total de 258 muestras, se extrajo ADN para su purificación y cuantificación, posteriormente se realizó la genotipificación del exón 8 mediante PCR en tiempo real y utilizando el software applied biosystems, las asociaciones genotípicas (con SNP y sin SNP) se realizaron por la prueba estadística de Razón de Momios. Resultados. El 72.87 % de la población estudiada presenta dislipidemias, siendo la hipoαlipoproteinemia la más frecuente, mayormente en hombres (78.19 %). Se encontró asociación protectora limítrofe para hipoαlipoproteinemia quienes presenten el SNP rs5888 y asociación de riesgo en hipercolesterolemia e hipertrigliceridemia, concluyendo que un cambio en el mismo gen, pueden aumentar o disminuir la probabilidad de desarrollar enfermedades cardiovasculares.

This study introduces the design of an asynchronous event-driven adaptive robust control that regulates a mobile limb orthosis position for the hind legs of a Canis lupus familiaris (CLF). The application of a suitable stability analysis based on a controlled Lyapunov function results in the laws to adjust the adaptive gains of a proportional integral derivative controller (PID). The controller succeeded in compensating external bounded perturbations and non-modeled uncertainties in the active orthosis device. This compensation forces the tracking between the current positions and some reference trajectories obtained by a biomechanical gait cycle analysis of the CLF. The controller starts with the event triggered from the power of the electromyography signal from the frontal legs. If the signal power is higher than a predefined threshold, the movement of the orthosis will initiate. Electromyographic signals were acquired offline and injected into a virtualized orthosis model to test the event-driven control design. A set of numerical simulations confirmed a better performance of tracking reference trajectories and the effect of the event-driven controller on the orthosis operation. The experimental validation of the proposed output feedback controller on the designed orthosis seems to justify a potential automatized rehabilitation therapy based on the proposed electromyography-driven strategy.

This study describes the design, instrumentation and control of an exoskeleton for lower limb children rehabilitation with nine degrees of freedom. Three degrees of freedom in each leg exert the movements of hip, knee and ankle in the sagittal plane, and three control the drive track system composed by a caterpillar-like robot. The control scheme presents a model free decentralized output feedback adaptive high-order sliding mode control to solve the trajectory tracking problem in each degree of freedom of the exoskeleton. A high order sliding mode differentiator estimates the unmeasured states and, by means of a dynamical state extension, it approximates the unknown dynamical model of the exoskeleton. A second-order adaptive sliding mode controller based on the super-twisting algorithm drives the exoskeleton articulations to track the proposed reference trajectories, inducing an ultimate boundedness for the tracking error. Numerical and experimental simulation results demonstrate the effect of the adaptive gain on the super-twisting control design. Such evaluations confirmed the superior tracking performance forced by the adaptive law for the controller with a smaller chattering amplitude and smaller mean tracking error.

This manuscript deals with the problem of controlling a virtualized humanoid robot with 16 degrees of freedom (DOF). The aim of this study was to design an output feedback discontinuous controller ...

This manuscript describes the design and numerical implementation of a novel composite differential neural network aimed to estimate nonlinear uncertain systems. A differential neural network (DNN) with a composite feedback matrix approximates the structure of non-linear uncertain systems. The feedback matrix is assumed to belong to a convex set as well as the free parameters of the DNN (weights) at any instant of time. Therefore, ɩ-different DNN works in parallel. A composite Lyapunov function finds the convex hull approximation of the set of DNN working together to improve the approximation capabilities of classical neural networks. The main result of this study shows the practical stability of the estimation error. Numerical simulations demonstrate the approximation capabilities of the composite DNN implemented in a Van Der Pol oscillator where the presence of high-frequency components makes difficult a classical DNN approximation.

The aim of this study was to design a constrained robust output feedback control strategy for stabilizing linear systems affected by uncertainties and output perturbations. The input is co...

Accurate and fast estimation of single-phase grid voltage phase and frequency has the potential to improve the performances of various control and monitoring techniques used in electric power systems. This letter applies an adaptive sliding mode observer for frequency and phase estimation. The observer is simple, easy to tune, and suitable for real-time implementation. The proposed adaptive observer can be considered as an alternative to a phase-locked loop (PLL) with better performance. dSPACE-based experimental results are given to show the effectiveness and performance improvement of the proposed approach with respect to two other advanced PLL techniques, namely, pseudolinear enhanced PLL and second-order generalized integrator-frequency locked loop.

The non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model. Complementary, input estimation considers a different problem where the model and the output are known. However, if neither model nor input is known, the identification problem seems to be more complicated. This is a major challenge in electrophysiological systems where the output signal can be measured, but the biological system is uncertain and the stimuli are unknown. The aim of this study was to introduce a novel methodology that may estimate the uncertain input in this more complex situation where the biological model of the system under analysis is uncertain and the input stimuli must be estimated accurately. A two step non-parametric identification scheme based on differential neural networks (DifNN) and the second order super-twisting sliding mode algorithm (STA) identified the input stimulus. The STA makes a robust exact estimation of the output signal. The DifNN produces an internal non-parametric mathematical modeling of the input-output relationship executed without the input information. Learning laws for both levels of DifNN modeling were presented as part of the uncertain input identification process. The estimation of uncertain visual stimulus applied over the retina in an ex-vivo avian model (EVAM) served to test the method proposed in this study. The output information corresponded to the electrophysiological voltage variation in the optical nerve.

This article shows the design of a robust second-order sliding mode controller to solve the trajectory tracking problem of an active orthosis for assisting back physiotherapies. The orthosis was designed in agreement with morphological dimensions and its articulations distribution followed the same designing rules. The orthosis has six articulated arms attached to an articulated column. The orthosis was fully instrumented with actuators and position sensors at each articulation. The controller implemented a class of hybrid/position controller depending on the relative force exerted by the patient and the orthosis movement. The position information provided by each articulation was supplied to a distributed super-twisting differentiator to recover the corresponding angular velocity. A set of twisting controllers was implemented to regulate the position of the robot in agreement to predefined reference trajectories. Reference trajectories were obtained from a biomechanical-based analysis. The hybrid tracking control problem solved the automation of the assisted therapy to the patient, including the force feedback. The performance of the orthosis was tested with different dummy bodies with different resistance. The robust output feedback controller successfully tracked the reference trajectories despite the material of the dummy used during the testing. The orthosis was evaluated with two volunteers using a simple reference trajectory. Graphical Abstract General structure of the active back assisted orthosis.

This manuscript introduces a novel methodology to solve the state estimation of discrete-time multi-input multi-output (MIMO) nonlinear systems with uncertain dynamics. The mathematical model of the nonlinear systems considered in this paper satisfies the usual Lagrangian structure that characterizes many mechanical, electrical or electromechanical systems. A recurrent neural network (RNN) estimates the uncertain dynamics of the MIMO system with an updating law based on a particular variant of the discrete-time version of the super-twisting algorithm (DSTA). A Lyapunov stability analysis defines the convergence zone for the state estimation error throughout the solution of a matrix inequality. The convergence zone for the estimation is smaller when the DSTA and the RNN work together in an observer. Numerical examples demonstrate how the adaptive observer reduces the zone of convergence and the oscillations in the steady state compared with a discrete version of the STA with additional linear correcting terms. An experimental implementation shows how the observer estimates the unknown states of a Van Der Pol Oscillator. A comparison against some variations of the DSTA justifies the advantages of the mixed DSTA-RNN observer.

This study evaluates naproxen (NP) degradation efficiency by ozonation using nickel oxide films (NiO(F)NiO(F)) as a catalyst. The NiO films were synthesized by chemical vapor deposition and characterized by X-ray diffraction, scanning electron microscopy, atomic force microscopy and X-ray photoelectron spectroscopy. NP degradation was conducted for 5 min using 10 films of NiO(F) comparing against ozonation using 100 mg/L NiO powder in suspension (NiO(s)NiO(S)) and conventional ozonation (O3-conv). Total organic carbon analysis demonstrated a mineralization degree of 12% with O3O3-conv, 35% with NiONiO as powder and 22% with NiO(F)NiO(F) after 60 min of reaction. The films of NiO(F)NiO(F) were sequentially used 4 times in ozonation demonstrating the stability of the synthesized material, as well as its properties as a catalyst for ozonation. A proposed modeling strategy using robust parametric identification techniques allows the comparison of NP decomposition pseudo-monomolecular reaction rates.

Unsupervised learning is a target free methodology to classify unorganized information. This study proposes a new unsupervised learning method for classifying unlabeled targets based on convex ellipsoidal sets. The method described here uses ellipsoidal calculus tools to realize pattern classification via a clustering scheme. The algorithm consisted in adjusting the number of clusters defined by an ellipsoidal set as well as their centers, shape forms and orientation. All these processes were realized without preliminary information on the data distribution. The application of an inner gradient descent algorithm permitted the adjustment of these parameters, using the standard deviation of data in the cluster with respect to the semi-axis of ellipsoid that contains the corresponding data. A specific evaluation of the proposed algorithm used two-dimensional attributes databases. The outcomes of the classification results were compared with those produced by the K-means and DBSCAN algorithm. Comparable classification results were achieved overcoming some drawbacks of the methods.

This manuscript presents an algorithm to classify continuously electroencephalographic (EEG) signals based on deep differential neural networks (DDNNs). The learning laws are obtained by the second stability method of Lyapunov that requires the solution of a set of matrix differential equations. The robustness of this technique allows the analysis and classification of bio-signals like EEG signals. The EEG signals have complex dynamics and they are strongly affected by noises in the measurements and a high degree of variability between different studies in patients. The main strength of DDNNs is their feedback property, which allows them to work with the time-dependent variation of the EEG signals. The DDNNs are tested in a database constituted of EEG signals acquired from a study made in ten volunteers. The study consisted of the acquisition of EEG measurements of the volunteers recognizing geometrical figures appearing in a graphic user interface. The DDNN obtained better performance than a single layer differential neural network and a convolutional neural network.

This study focuses in the output feedback stabilisation of constrained linear systems affected by uncertainties and noisy output measurements. The system states are restricted inside a given polyto...

In this paper, a differential neural network (DNN) implemented as a robust observer estimates the dynamics of perturbed uncertain nonlinear systems affected by exogenous unknown inputs. In the first stage, the identification error converges into a neighborhood around the origin. Then, the second-order sliding mode supertwisting algorithm implemented as a robust exact differentiator reconstructed the unknown inputs. The approach proposed in this paper can be applied in the case of full access to the state vector (identification problem) and in the case of partial access to the state vector (estimation problem). In the second case, the nonlinear system under study must have well-defined full relative degree with respect to the unknown input. Numerical examples showed the effectiveness of the proposed algorithm. The first example tested the DNN working as an identifier into a mathematical model describing the dynamics of a spatial minisatellite. The second example (with a DNN implemented as an observer) tested the methodology of this paper over a single link flexible robot manipulator represented in a canonical (Brunovsky) form. In both examples, the mathematical models served as data generators in the testing of the neural networks. Even when not exact mathematical description of both models was used in the input estimation, the accuracy obtained with the DNN is comparable with the case of applying a high-order differentiator with complete knowledge of the plant.

This study designed a suboptimal output control strategy to characterise an attractive and invariant set for the state trajectories of perturbed linear systems with noisy measurements and state constraints. The state constraints were defined by a given polytope formed of by n-dimensional vectors. An adaptive linear controller enforced the existence of an attractive and invariant set (centred at the origin) for the trajectories of the perturbed system. A barrier Lyapunov function (BLF) and the attractive ellipsoid method (AEM) derived the adjustment law of the adaptive gain. The controller design used the linear matrix inequality technique to solve two optimisation problems. The first solution provided a maximal set where the initial conditions must belong without violating the state constraints. The second optimisation solution characterised the invariant minimal attractive set for the system trajectories. An academic example verified how the proposed adaptive control generated the system trajectories that converged to the minimal attractive ellipsoid while keeping them inside the polytope defining the state constraints. The simulation script showed the advantages of the adaptive BLF controller (ABLC) against classical AEM controller. A second numerical example considered a direct current motor showing the advantages of the ABLC against the sliding mode technique.

Robust synchronization of master slave chaotic systems are considered in this work. First an approximate model of the error system is obtained using the ultra-local model concept. Then a Continuous Singular Terminal Sliding-Mode (CSTSM) Controller is designed for the purpose of synchronization. The proposed approach is output feedback-based and uses fixed-time higher order sliding-mode (HOSM) differentiator for state estimation. Numerical simulation and experimental results are given to show the effectiveness of the proposed technique.

This study deals with the design of an automatic controller to solve the problem of trajectory tracking for collaborative robot manipulators. The control algorithm corresponds to an adaptive proportional derivative controller (APDC) aimed to reduce the needed energy to execute the tracking of some predefined reference trajectories. The controller was designed with the aim of keeping a constant distance between the final effectors of both manipulators. The derivative estimation used in the APDC was obtained by a set of robust exact differentiators based on the application of the super-twisting algorithm (STA). The controller was tested on two real cooperative multi-articulated robotic manipulators system. The experimental setup consisted of two robot manipulators designed with four degrees of freedom each one constructed by the 3D printing technology. The experimental results included comparisons with respect to the classical non-adaptive proportional-derivative controllers. The superior performance of the adaptive controller proposed in this study was confirmed by the estimation of the least mean square analysis of the tracking error.

Real instrumentation of control systems in digital devices introduces the necessity of considering quantization and sampling information used in the control and estimator design. The aim of this study is designing a state estimator for uncertain second order nonlinear systems based on the approximation enforced by differential neural networks (DNN) with quantized and time-varying sampled output information. The effect of sampling output information is considered as a time-varying delay. The DNN estimates the set of non-linearities in the system structure with the applications of an adaptive approximation. A Lyapunov-Krasovskii functional served to justify the design of the law that adjusted the weights of the DNN. The origin of the estimation error space is practically stable with the approximation enforced by the DNN. Experimental results implement the DNN observer to reconstruct the states of the Van Der Pol Oscillator. The estimation attained with the proposed observer is compared with the results provided by classical linear observer. The evaluation of the least mean square error demonstrates the superior performance of the solution suggested in this study. The Lyapunov-Krasovskii methodology estimates the region of convergence depending on the sampled period and the level of quantization.

This paper presents the design and implementation of an output feedback controller based on the super twisting algorithm (STA) that stabilizes the trajectory tracking error of a skid steered mobile robot (SSMR). The control scheme introduces a diffeomorphism based on the mathematical model of the SSMR to transform the original problem into a third order chain of integrators. In this study, the available measurements are the position and orientation of the SSMR. A modified STA working as a step by step differentiator estimates the velocity and acceleration of the mobile robot. Then, a second STA enforces the tracking of a predefined trajectory. Numerical and experimental results comparing the STA with a state feedback controller (SFC) and a first order sliding mode controller (FOSM) justify the control proposal.

Surgical and medical robotics had become an interesting field of research joining disciplines such as biomedical engineering, mechatronics and automatic control. One of the main design issues of surgical robots is the interaction (contact force) between the end effector and the biological tissue. This manuscript presents the design, instrumentation and control of a surgical robot prototype (SRP) with two robotic arms of three degrees of freedom (DoF) attached to a cartesian robot, getting a total of five DoF per arm. The end effector consists of a robotic scalpel instrumented with force sensors to interact with the biological tissue resulting in more precise cuttings and avoiding, at the same time, damage of surrounding areas. An output-feedback proportional derivative hybrid controller is applied to track the trajectories aimed to realized precise cut. Simulations and experimental results show the performance of the SRP.

This manuscript deals with the problem of controlling a virtualized humanoid robot with 16 degrees of freedom (DOF), each corresponding to the articulation in a real human being. That is, three DOF for each leg in the sagittal plane and one for the abduction movement; three DOF for each arm in the sagittal plane and one for the waist. The tracking trajectory problem of any humanoid robot like the classical biped robots requires a control algorithm with robustness against parametric uncertainties, fast response and even with finite-time convergence. These main characteristics are easily covered by sliding mode controllers. This manuscript implements a terminal second order sliding mode (TSOSM) controller to ensure the finite-time tracking trajectory of each articulation of the humanoid robot to the ones that define a classical walking pattern obtained by bio-mechanical studies. The TSOSM is implemented in a virtual platform developed in a computer-aided design software.

One of the critical aspects in modelling soil organic carbon (SOC) predictions is the lack of access to soil information which is usually concentrated in regions of high agricultural interest. In Chile, most soil and SOC data to date is highly concentrated in 25 % of the territory that has intensive agricultural or forestry use. Vast areas beyond those forms of land use have few or no soil data available. Here, we present a new database of SOC for the country, which is the result of an unprecedented national effort under the frame of the Global Soil Partnership that help to build the largest database on SOC to date in Chile named “CHLSOC" comprising 13,612 data points. This dataset is the product of the compilation from numerous sources including unpublished and difficult to access data, allowing to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC range from 6×10−5 to 83.3 percent, reflecting the variety of ecosystems that exists in Chile. Profiting from the richness of geochemical, topographic and climatic variability in Chile, the dataset has the potential to inform and test models trying to predict SOC stocks and dynamics at larger spatial scales., Earth System Science Data Discussions, ISSN:1866-3591

This chapter is devoted to the problem of robust synchronization of master-slave chaotic systems. By using the notion of relative degree concept, an approximate model of the error system is first obtained. The approximate model bundles together the nonlinearities, disturbances/perturbations, etc., as an unknown input. Then a continuous singular terminal sliding-mode controller is designed for the approximate error model to achieve consensus among the master and slave system. This chapter assumes that only the output information is measurable/available. As such, a higher-order sliding-mode observer is used for the state and unknown input estimation. Numerical simulation using Duffing-Holmes type chaotic oscillator and experimental results using Van der Pol type oscillators are given to demonstrate the performance of the proposed technique.

The objective of this study was to design of an output based impedance adaptive controller for a special class of cervical orthoses, a class of biomedical devices for the rehabilitation of neck illnesses. The controller used the adaptive sliding mode theory to enforce the tracking of the reference trajectory if the patient was not resistant to the therapy. If the patient rejects the orthosis activity, a second impedance-based controller governs the orthosis movement allowing the patient to take the leading role in the orthosis sequence of movements. The proposed controller considers a weighted controller combining the tracking and the impedance controls in a single structure. The monitoring of the external force was evaluated on a novel weighting function defining on-line the role of each controller. The proposed orthosis was motivated by the prevalence of whiplash, which is a syndrome that is produced by forced hyperextension and hyperflexion of the neck. This study included the development of a technological prototype of the orthotic type to support the recovery of patients diagnosed with whiplash. The sections that make up the orthotic device are two independent systems that move the patient’s head in the sagittal and frontal planes. For this purpose, the mechanical structure of the cervical orthosis was made up of 7 pieces printed in 3D with polylactic acid (PLA). The operation of the cervical orthosis was evaluated in two sections: (a) using a simulation system, which consists of a spring with an artificial head and the development of a graphic interface in Matlab, and (b) evaluating the controller on the proposed orthosis. With these elements, the follow-up of the trajectory proposed by the actuators was evaluated, as well as its performance in the face of the opposition that a patient generates. The superiority of the proposed controller was confirmed by comparing the tracking efficiency with proportional-integral-derivative and first-order sliding variants.

This paper deals with the robust stabilization of input saturated uncertain linear systems with perturbed measurements. The controller designed as a linear output feedback allowed to characterize the region of attraction where the initial conditions must belong in order to guarantee the asymptotic convergence of the system trajectories into a ultimately bounded region around the origin. The stability analysis employed the concept of Barrier Lyapunov functions (BLF) and the attractive ellipsoid method (AEM) to find sufficient conditions in terms of linear matrix inequalities characterizing the ultimate bounded region of attraction. The methodology proposed is to use the BLF to estimate the region of attraction, while the AEM successfully characterized the ultimately bound. And optimization procedure maximized the set estimating (in some sense) the region of attraction and minimized the ultimate bounded set. Simulation results showed the accuracy in the estimation of the region of attraction and the minimal ellipsoid characterization.

Most of the research in sliding mode theory has been carried out to in continuous time to solve the estimation and control problems. However, in discrete time, the results in high order sliding modes have been less developed. In this paper, a discrete time super-twisting-like algorithm (DSTA) was proposed to solve the problems of control and state estimation. The stability proof was developed in terms of the discrete time Lyapunov approach and the linear matrix inequalities theory. The system trajectories were ultimately bounded inside a small region dependent on the sampling period. Simulation results tested the DSTA. The DSTA was applied as a controller for a Furuta pendulum and for a DC motor supplied by a DSTA signal differentiator. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

The aim of this study was to design an adaptive control strategy based on recurrent neural networks RNNs. This neural network was designed to obtain a non-parametric approximation identification of discrete-time uncertain nonlinear systems. A discrete-time Lyapunov candidate function was proposed to prove the convergence of the identification error. The adaptation laws to adjust the free parameters in the RNN were obtained in the same stability analysis. The control scheme used the states of the identifier, and it was developed fulfilling the necessary conditions to establish a behavior comparable with a quasi-sliding mode regime. This controller does not use the regular form of the switching function that commonly appears in the sliding mode control designs. The Lyapunov candidate function to design the controller and the identifier simultaneously requires the existence of positive definite solutions of two different matrix inequalities. As consequence, a class of separation principle was proven when the RNN-based identifier and the controller were designed by the same analysis. Simulations results were designed to show the behavior of the proposed controller solving the tracking problem for the trajectories of a direct current DC motor. The performance of the proposed controller was compared with the solution obtained when a classical proportional derivative controller and an adaptive first-order sliding mode controller assuming poor knowledge of the plant. In both cases, the proposed controller showed superior performance when the relation between the tracking error convergence and the energy used to reach it was evaluated. Copyright © 2016 John Wiley & Sons, Ltd.

A robust control feedback strategy is developed to solve the stabilization problem of constrained systems with uncertainties and output perturbations. The states are assumed to be constrained inside a given polytope and the perturbations bounded. The control law is developed using an extended version of the attractive ellipsoid method (AEM) approach, and a barrier Lyapunov function (BLF); this is a function whose value goes to infinity whenever its arguments approach to the boundary of a given set. The control parameters are obtained through the solution of some optimization problems related to the approximation of the constraints set and the characterization of a minimal ultimate bounded set for the system trajectories. The implementability of the resulting algorithm is supported by a numerical example and by the comparison with the regular AEM based on a quadratic Lyapunov function.

This study presents an event driven automatic controller to regulate the movement of a mobile lower limb active orthosis (LLAO) triggered with the information obtained from electromyographic (EMG) signals, which are captured from the user’s triceps and biceps muscles. The proposed controller has an output feedback realization including a velocity estimator algorithm based on a high order sliding mode observer. The output feedback controller implements a class of decentralized super-twisting algorithm. The controller must enforce the movement of the orthosis articulations following some defined reference trajectories. This strategy realizes a time-window dependent event driven controller for the active orthosis. The controller selects among four different routines to be executed by a patient. A differential neural network classifies the different patterns of muscle movements. This classifier succeeds in defining the correct EMG class in a 95% of the tested signals. This work senses the EMG signals from the biceps and triceps, considering a possible injury in the patient to be obtained from the quadriceps. Therefore, four upper limb routines are established to generate the corresponding classes and the four different main therapies for the LLAO. A fully instrumented and self-designed orthosis is constructed to evaluate the proposed controller including three rotational joints per leg and a mobile robot to execute translation movements.