This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a $priori$ knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

Observer-Based Fuzzy Adaptive Event-Triggered Control for Pure-Feedback Nonlinear Systems With Prescribed Performance

Auto-weighted multi-view co-clustering with bipartite graphs

This article addresses the problem of continuous-time dynamic sliding mode control for Takagi–Sugeno fuzzy nonlinear systems based on an event-triggered mechanism. First, a novel sliding mode dynamic system is established by introducing a corresponding sliding mode function and an efficient event-triggered mechanism. Then, based on a new Lyapunov function and the reciprocally convex method, sufficient conditions are presented to guarantee that the constructed network sliding mode dynamics is asymptotically stable. It is shown that the trajectories of the sliding mode dynamic system can converge into a bounded domain of a sliding mode surface in finite time. Furthermore, the explicit expression of the desired sliding mode controller is given in the form of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed design techniques are demonstrated by a numerical example.

Event-Triggered Fuzzy Control for Nonlinear Systems via Sliding Mode Approach

The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.

Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks

Actuators nonlinearities are unknown external perturbations in robots, which are unwanted because they can severely limit their performance. This research is focused on the stabilization of robots subject to actuators nonlinearities with a regulator containing the sigmoid mapping. Our regulator has the following three main characteristics: a) a sigmoid mapping is used to ensure boundedness of the regulator law terms, b) the chattering is reduced by the usage of the saturation mapping instead of the signum mapping, and c) the stabilization is ensured by the Lyapunov analysis. Finally, we evaluate our regulator for the stabilization of two robots.

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Stabilization of Robots With a Regulator Containing the Sigmoid Mapping

In this work, we study the structure regulator for the perturbations attenuation which is based on the infinite structure regulator. The structure regulator is able to attenuate the perturbations if the transfer function of the departures and perturbations has a numerical value almost equal to zero, and it does not require the perturbations to attenuate them. We apply the structure regulator and the infinite structure regulator to a quadrotor which maintains the horizontal position with respect to the earth for the step and sine perturbations.

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Structure Regulator for the Perturbations Attenuation in a Quadrotor

ANFIS system for classification of brain signals

In this work, the modelling and regulation of two mechanical systems are studied. The model is employed to describe the system dynamic behaviour. The regulator is employed to force the system states to track constantly wanted signals. The stability of the advised regulator is analysed via the Lyapunov technique. The recommended strategy is applied to a magnetic generator and in a hexarotor.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2017.0521

Modelling and regulation of two mechanical systems

In this paper, the mathematical model of an electricity generator with four static magnets and four dynamic magnets is suggested for the electricity generation. The introduced electricity generator is based on the electricity generator with two static magnets and two dynamic magnets, but it has three main advantages: it has more interactions between the static and dynamic magnets, it generates more electricity, and it generates smoother signals. Electricity generators are compared for the electricity generation.

Arturo Aguilar

Papers

Structure Regulator for the Perturbations Attenuation in a Quadrotor

Stabilization of Robots With a Regulator Containing the Sigmoid Mapping

ANFIS system for classification of brain signals

Modelling and regulation of two mechanical systems

An Electricity Generator Based on the Interaction of Static and Dynamic Magnets