Adaptive Neural Network-Based Observer Design for Switched Systems With Quantized Measurements

A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors

Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors.

A novel sentiment analysis method based on multi-scale deep learning

As the college students have been a most active user group in various social media, it remains significant to make effective sentiment analysis for college public opinions. Capturing the direction of public opinion in the student community in a timely manner and guiding students to develop the right values can help in the ideological management of universities. Universally, the recurrent neural networks have been the mainstream technology in terms of sentiment analysis. Nevertheless, the existing research works more emphasized semantic characteristics in vertical direction, yet failing to capture sematic characteristics in horizonal direction. In other words, it is supposed to increase more balance into sentiment analysis models. To remedy such gap, this paper presents a novel sentiment analysis method based on multi-scale deep learning for college public opinions. To fit for bidirectional semantic characteristics, a typical sequential neural network with two propagation paths is selected as the backbone. It is then extended with more layers in horizonal direction. Such design is able to balance both model depth and model breadth. At last, some experiments on a real-world social media dataset are conducted for evaluation, well acknowledging efficiency of the proposed analysis model.

Design of a control mechanism for the educational management automation system under the Internet of Things environment

Since the entrance of the Internet era, management automation has been an inevitable tendency in many areas. Especially, the great progress of Internet of Things (IoT) in recent years has provided more convenience for basic data integration. This also boosts the development of various management automation systems. In this context, this paper takes physical education as the object, and proposes the design of a control mechanism for educational management automation systems under the IoT environment. First, a description with respect to the overall design, detailed design, and database design is given. In addition, a low-consumption flow table batch update mechanism is studied, which packages and distributes the update rules of all nodes to be updated, in order to reduce the communication consumption between the controller and nodes. The results show that the education management automation of the college gymnasium can be well realized by using the optimization control mechanism. It cannot only make reasonable adjustments to college sports resource data, basic equipment, etc., but also improves the quality of resource management of college physical education courses to ensure that college sports resources can be used in all aspects, and further improves the operating efficiency of the sports management system. The automation technology design of the college sports management system can improve the efficiency of college sports management by more than 20%, so as to ensure the comprehensive development of students in physical education courses and promote the rapid improvement of college management level.

Disturbance observer-based adaptive position control for a cutterhead anti-torque system

To conveniently replace worn cutterhead tools in complicated strata, a novel cutterhead attitude control mechanism was recently designed. Meanwhile, the mechanism also causes an engineering problem of how to control a matching cutterhead anti-torque system (CATS) effectively, which is used to prevent a drive box of the cutterhead from rotation during a complex excavation process. In this paper, a disturbance observer-based adaptive position controller is proposed for the CATS. The proposed method presents a nonlinear adaptive controller with adaptation laws to compensate for the unknown time-varying load torque and damping uncertainty in the system. Based on the disturbance observer method and sliding mode control, an asymptotically stable controller proven by Lyapunov theory is constructed using the back-stepping technique. In addition, a virtual test rig based on MATLAB and AMESim co-simulation is built to verify the validity of the proposed controller. The simulation results show that the proposed method has good performance for tracking tasks in the presence of uncertainties compared with PID control. Together, the data support targeting disturbance observer-based adaptive position control as a potential control strategy for cutterhead anti-torque systems.

User-Independent EMG Gesture Recognition Method Based on Adaptive Learning

In a gesture recognition system based on surface electromyogram (sEMG) signals, the recognition model established by existing users cannot directly generalize to the across-user scenarios due to the individual variability of sEMG signals. In this article, we propose an adaptive learning method to handle the problem. The muscle synergy is chosen as the feature vector because it can well-characterize the neural origin of movement. The initial train set is composed of representative samples extracted from the synergy matrix of the existing user. When the new users use the system, the label is obtained by the adaptive K nearest neighbor algorithm (KNN). The recognition process does not require the pre-experiment for new users due to the introduction of adaptive learning strategy, namely, the qualified data and the label of new user data evaluated by a risk evaluator are used to update the train set and KNN weights, so as to adapt to the new users. We have tested the algorithm in DB1 and DB5 of Ninapro databases. The average recognition accuracy is 68.04, 73.35, and 83.05% for different types of gestures, respectively, achieving the effects of the user-dependent method. Our study can avoid the re-training steps and the recognition performance will improve with the increased frequency of uses, which will further facilitate the widespread implementation of sEMG control systems using pattern recognition techniques.

Fixed-time sliding mode attitude control of a flexible spacecraft with rotating appendages connected by magnetic bearing

This study focuses on the attitude control of a flexible spacecraft comprising rotating appendages, magnetic bearings, and a satellite platform capable of carrying flexible solar panels. The kinematic and dynamic models of the spacecraft were established using Lagrange methods to describe the translation and rotation of the spacecraft system and its connected components. A simplified model of the dynamics of a five-degrees-of-freedom (DOF) active magnetic bearing was developed using the equivalent stiffness and damping methods based on the magnetic gap variations in the magnetic bearing. Next, a fixed-time sliding mode control method was proposed for each component of the spacecraft to adjust the magnetic gap of the active magnetic bearing, realize a stable rotation of the flexible solar panels, obtain a high inertia for the appendage of the spacecraft, and accurately control the attitude. Finally, the numerical simulation results of the proposed fixed-time control method were compared with those of the proportional-derivative control method to demonstrate the superiority and effectiveness of the proposed control law.