Instituto Tecnólogico de La Laguna

In this paper, we present a controller design and its implementation on a mini rotorcraft having four rotors. The dynamic model of the four-rotor rotorcraft is obtained via a Lagrange approach. The proposed controller is based on Lyapunov analysis using a nested saturation algorithm. The global stability analysis of the closed-loop system is presented. Real-time experiments show that the controller is able to perform autonomously the tasks of taking off, hovering, and landing.

BACKGROUND AND OBJECTIVES: Obesity is an independent risk factor for development and progression of chronic kidney disease (CKD). We conducted a systematic review to assess the benefits of intentional weight loss in patients with non-dialysis-dependent CKD and glomerular hyperfiltration. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We searched MEDLINE, SCOPUS, and conference proceedings for randomized, controlled trials and observational studies that examined various surgical and nonsurgical interventions (diet, exercise, and/or antiobesity agents) in adult patients with CKD. Results were summarized using random-effects model. RESULTS: Thirteen studies were included. In patients with CKD, body mass index (BMI) decreased significantly (weighted mean difference [WMD] -3.67 kg/m(2); 95% confidence interval [CI] -6.56 to -0.78) at the end of the study period with nonsurgical interventions. This was associated with a significant decrease in proteinuria (WMD -1.31 g/24 h; 95% CI -2.11 to -0.51) and systolic BP with no further decrease in GFR during a mean follow-up of 7.4 mo. In morbidly obese individuals (BMI >40 kg/m(2)) with glomerular hyperfiltration (GFR >125 ml/min), surgical interventions decreased BMI, which resulted in a decrease in GFR (WMD -25.56 ml/min; 95% CI -36.23 to -14.89), albuminuria, and systolic BP. CONCLUSIONS: In smaller, short-duration studies in patients with CKD, nonsurgical weight loss interventions reduce proteinuria and BP and seem to prevent further decline in renal function. In morbidly obese individuals with glomerular hyperfiltration, surgical interventions normalize GFR and reduce BP and microalbuminuria. Larger, long-term studies to analyze renal outcomes such as development of ESRD are needed.

The design of robust tracking control for quadrotors is an important and challenging problem nowadays. In this paper, a robust tracking output-control strategy is proposed for a quadrotor under the influence of external disturbances and uncertainties. Such a strategy is composed of a finite-time sliding-mode observer, which estimates the full state from the measurable output and identifies some types of disturbances. It is also composed of a combination of PID controllers and continuous sliding-modes controllers that robustly track a desired time-varying trajectory with exponential convergence despite the influence of external disturbances and uncertainties. The closed-loop stability is provided based on the input-to-state stability (ISS) and finite-time ISS properties. Finally, experimental results in real time show the performance of the proposed control strategy.

In this paper, we present a semiglobal asymptotic stability analysis via Lyapunov theory for a new proportional-integral-derivative (PID) controller control scheme, proposed in this work, which is based on a fuzzy system for tuning the PID gains for robot manipulators. PID controller is a well-known set point control strategy for industrial manipulators which ensures semiglobal asymptotic stability for fixed symmetric positive definite (proportional, integral, and derivative) gain matrices. We show that semiglobal asymptotic stability attribute also holds for a class of gain matrices depending on the manipulator states. This feature increases the potential of the PID control scheme to improve the performance of the transient response and handle practical constraints in actual robots such as presence of actuators with limited torque capabilities. We illustrate this potential by means of a fuzzy self-tuning algorithm to select the proportional, integral, and derivative gains according to the actual state of a robotic manipulator. To the best of the authors' knowledge, our proposal of a fuzzy self-tuning PID regulator for robot manipulators is the first one with a semiglobal asymptotic stability proof. Real-time experimental results on a two-degree-of-freedom robot arm show the usefulness of the proposed approach.

A novel proportional-integral-derivative (PID)-type motion controller for a quadrotor is introduced in this paper. A rigorous analysis of the closed-loop system trajectories is provided, and gain tuning guidelines are discussed. Real-time experimental results consisting of the implementation of a PID-based scheme, a sliding-mode controller, and the new scheme are given. Gains are selected so that the three tested controllers present the same energy consumption. In order to assess the robustness of the controllers tested, experiments are carried out in the presence of disturbances in one of the actuators. Specifically, the disturbance consists in attenuating the force delivered. Better tracking accuracy is obtained with the introduced nonlinear PID-type algorithm.

In this paper, the problem of time-varying parameter identification is studied. To this aim, two identification algorithms are developed in order to identify time-varying parameters in a finite time or prescribed time (fixed-time). The convergence proofs are based on a notion of finite-time stability over finite intervals of time, i.e., short-finite-time stability, homogeneity for time-varying systems, and Lyapunov-based approach. The results are obtained under injectivity of the regressor term, which is related to the classical identifiability condition. The case of bounded disturbances (noise of measurements) is analyzed for both algorithms. Simulation results illustrate the feasibility of the proposed algorithms.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper proposes a natural saturating extension of the proportional-derivative (PD) with desired gravity compensation (PD<emphasis emphasistype="boldital">d</emphasis>gc) control law for the global regulation of robot manipulators with bounded inputs. Compared with other algorithms previously proposed under the same analytical framework, it proves to be advantageous in several senses. First, it involves a single saturation function at each joint. Second, it does not need to discriminate the terms that shall be bounded, since these are simply all of them included within the only saturation involved at every joint. Experimental results show the effectiveness of the proposed scheme. As far as the authors are aware, no such type of natural saturating extension (i.e., involving only one saturation function at each joint, where all the terms of the controller are embedded) to the bounded input case had been previously proposed for the PD<emphasis emphasistype="boldital">d</emphasis>gc scheme. Furthermore, it is shown how the proposed approach may be conceived within the framework of the energy shaping plus damping injection methodology. </para>

In this work, two globally stabilizing bounded control schemes for the tracking control of robot manipulators with saturating inputs are proposed. They may be seen as extensions of the so-called PD+ algorithm to the bounded input case. With respect to previous works on the topic, the proposed approaches give a global solution to the problem through static feedback. Moreover, they are not defined using a specific sigmoidal function, but any one on a set of saturation functions. Consequently, each of the proposed schemes actually constitutes a family of globally stabilizing bounded controllers. Furthermore, the bound of such saturation functions is explicitly considered in their definition. Hence, the control gains are not tied to satisfy any saturation-avoidance inequality and may consequently take any positive value, which may be considered beneficial for performance-adjustment/improvement purposes. Further, a class of desired trajectories that may be globally tracked avoiding input saturation is completely characterized. For both proposed control laws, global uniform asymptotic stabilization of the closed-loop system solutions towards the prespecified desired trajectory is proved through a strict Lyapunov function. The efficiency of the proposed schemes is corroborated through experimental results.

In this paper we present a controller design and implementation on a mini-rotorcraft having four rotors. A Lagrangian model of the helicopter was used for the controller synthesis. The proposed controller is based on Lyapunov analysis. Experimental results show that the controller is able to perform autonomously the tasks of taking-off, hovering and landing.

Pesticides are chemicals used in agriculture, forestry, and, to some extent, public health. As effective as they can be, due to the limited biodegradability and toxicity of some of them, they can also have negative environmental and health impacts. Pesticide biodegradation is important because it can help mitigate the negative effects of pesticides. Many types of microorganisms, including bacteria, fungi, and algae, can degrade pesticides; microorganisms are able to bioremediate pesticides using diverse metabolic pathways where enzymatic degradation plays a crucial role in achieving chemical transformation of the pesticides. The growing concern about the environmental and health impacts of pesticides is pushing the industry of these products to develop more sustainable alternatives, such as high biodegradable chemicals. The degradative properties of microorganisms could be fully exploited using the advances in genetic engineering and biotechnology, paving the way for more effective bioremediation strategies, new technologies, and novel applications. The purpose of the current review is to discuss the microorganisms that have demonstrated their capacity to degrade pesticides and those categorized by the World Health Organization as important for the impact they may have on human health. A comprehensive list of microorganisms is presented, and some metabolic pathways and enzymes for pesticide degradation and the genetics behind this process are discussed. Due to the high number of microorganisms known to be capable of degrading pesticides and the low number of metabolic pathways that are fully described for this purpose, more research must be conducted in this field, and more enzymes and genes are yet to be discovered with the possibility of finding more efficient metabolic pathways for pesticide biodegradation.

Computed-torque control is a well-known motion control strategy for manipulators which ensures global asymptotic stability for fixed symmetric positive definite (proportional and derivative) gain matrices. In this paper, we show that global asymptotic stability also holds for a class of gain matrices depending on the manipulator state. This feature increases the potential of the computed-torque control scheme to handle practical constraint in actual robots such as presence of friction in the joints and actuators with limited torque capabilities. We illustrate this potential by means of a fuzzy self-tuning algorithm to select the proportional and derivative gains according to the actual tracking position error. Experiments on a two degrees of freedom robot arm show the usefulness of the proposed approach.

One of the simplest and natural appealing motion control strategies for robot manipulators is the PD control with feedforward compensation. Although successful experimental tests of this control scheme have been published since the beginning of the eighties, the proof of global asymptotic stability has remained unattended until now. The contribution of this paper is to prove that global asymptotic stability can be guaranteed provided that the proportional and derivative gains are adequately selected. The performance of the PD control with feedforward compensation evaluated on a two degrees-of-freedom direct-drive arm appears as fine as the classical model-based computed torque control scheme.

This brief proposes two alternative approaches for the global regulation of robot manipulators with input saturations. They prove to be simple extensions of the PD-with-gravity-compensation (PDgc) control law to the bounded-input case. Moreover, they turn out to be in a better position to approach (within the restricted range of the control variables) a PDgc control signal than other algorithms previously proposed under the same analytical framework. Closed-loop performance improvements are, therefore, obtained through their implementation. This is corroborated through experimental results

This article presents a comparison of three control techniques: nested saturations, backstepping, and sliding modes. The control objective consists of obtaining the best control strategy to stabilize the position of a quad-rotor unmanned aerial vehicle (UAV) when using visual feedback. We propose a vision-based method to measure translational speed as well as the UAV 3D position in a local frame. The three selected controllers were implemented and tested in real-time experiments. The obtained results demonstrate the performance of such methodologies applied to the quad-rotor system.

Breast cancer is the most common cancer among women worldwide with about half a million cases reported each year. Mammary thermography can offer early diagnosis at low cost if adequate thermographic images of the breasts are taken. The identification of breast cancer in an automated way can accelerate many tasks and applications of pathology. This can help complement diagnosis. The aim of this work is to develop a system that automatically captures thermographic images of breast and classifies them as normal and abnormal (without cancer and with cancer). This paper focuses on a segmentation method based on a combination of the curvature function k and the gradient vector flow, and for classification, we proposed a convolutional neural network (CNN) using the segmented breast. The aim of this paper is to compare CNN results with other classification techniques. Thus, every breast is characterized by its shape, colour, and texture, as well as left or right breast. These data were used for training as well as to compare the performance of CNN with three classification techniques: tree random forest (TRF), multilayer perceptron (MLP), and Bayes network (BN). CNN presents better results than TRF, MLP, and BN.

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The joint position regulation problem for robot manipulators under a standard saturated proportional-integral differential (PID) compensator is studied in this brief. The main result states the existence of PID control gains yielding semiglobal asymptotic stability if the control torque bounds are larger than gravitational torques. Energy shaping plus damping injection methods, as well as singular perturbation analysis, are used to establish stability conditions to achieve regulation at any desired position. Some experiments are carried out to illustrate the stability results.

This paper presents a literature review on magnetic gears, highlighting the advantages of using these technologies for mechanical power transmission applications in wind energy conversion systems and transportation, such as in electric vehicles. Magnetic gear technologies have important advantages over their mechanical counterparts. They can perform the speed change and torque transmission between input and output shafts by a contactless mechanism with a quiet operation and overload protection without the issues associated with conventional mechanical gears. The paper describes the fundamentals and operating principle of the field-modulated magnetic gear topologies and investigates the magnetic torque transmission mechanism. However, despite all the advantages highlighted in different research and development reports, there is still no convincing evidence to show that magnetic gear technologies are an acceptable alternative for industrial applications. The aim of this paper is to summarize previous work on magnetic gears to identify the topologies most suited for mechanical power transmission systems in wind energy conversion systems and electric vehicle applications. These applications will show that research and development of magnetic gear technologies contribute significantly to solutions for sustainable systems, a subject to which our current civilization must pay a lot of attention.

The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.

This paper addresses the set-point control of robot manipulators with friction where avoiding saturation of the actuators is a major issue. The original contribution is a novel direct fuzzy control system dealing with both practical constraints in mechanical manipulators: saturation and friction. The control system is made by taking advantage of input-output properties of the so-called sectorial fuzzy controllers. When friction is considered, we prove, via Lyapunov theory, that the steady state position errors owing to static friction are inside of a global attractor, which can be arbitrarily reduced. In case of absence of friction, the closed-loop system becomes globally asymptotic stable. In both cases, the important theoretical and practical feature of maintaining the control actions always within prescribed limits according to the actuator torque capabilities is guaranteed. Experimental evaluation of the proposed direct fuzzy control system on a nonlinear direct-drive robot arm is presented to validate its effectiveness.