In this paper, the balance control of a ball and beam system is considered. Based on the T-S fuzzy modeling, the dynamic model of the ball and beam system is formulated as a strict feedback form with ...modeling errors. Then, an adaptive dynamic surface control (DSC) is utilized to achieve the goal of ball positioning subject to parameter uncertainties. The robust stability of the closed-loop system is preserved by using the Lyapunov theorem. In addition to simulation results, the proposed T-S fuzzy model-based adaptive dynamic surface controller is applied to a real ball and beam system for practical evaluations. Simulation and experimental results illustrate that the proposed control scheme has much better performance than that of conventional DSC. Furthermore, parameter uncertainties and external disturbance are considered to highlight the robustness of the proposed control scheme.
Visually challenged people (VCPs) face many difficulties in their routine life. Usually, in many cases, they need to depend upon others, which makes them unconfident in an unfamiliar environment. ...Thus, in this paper, we present an aid that helps in detecting obstacles and water puddles in their way. This system comprises a walking stick and Android-based applications (APPs). The walking stick is embedded with Raspberry Pi and programmable interface controller (PIC) as a control kernel, sensors, a global position system (GPS) module, and alert-providing components. Sensors help to detect obstacles, and the VCP is informed through vibrations or a buzzer according to the obstacle detected. The GPS module receives the coordinates of the VCP's location, and the location can be tracked by parents using an APP. Another important APP is used, called an emergency APP, by which the VCP can communicate with parents or friends immediately by just shaking his/her cell phone or pushing the power button four times in 5 s in panic situations. We used fewer components to make the device simple, lighter, and cozy with very good features. This device will help VCPs to live an independent life up to some extent (with security), which ultimately will increase their confidence level in an unknown environment.
This paper presents a new robust adaptive control method for a class of nonlinear systems subject to uncertainties. The proposed approach is based on an adaptive dynamic surface control, where the ...system uncertainties are approximately modeled by interval type-2 fuzzy neural networks. In this paper, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all error signals are shown to be uniformly ultimately bounded. In addition to simulations, the proposed method is applied to a real ball-and-beam system for performance evaluations. To highlight the system robustness, different initial settings of ball-and-beam parameters are considered. Simulation and experimental results indicate that the proposed control scheme has superior responses, compared to conventional dynamic surface control.
Predictive maintenance techniques can determine the conditions of equipment in order to evaluate when maintenance should be performed. Thus, it minimizes the unexpected device downtime, lowers the ...maintenance costs, extends equipment lifecycle, etc. Therefore, this article developed a predictive maintenance mechanism with the construction of a test platform and data analysis along with machine learning. The information transmission of sensors was based on Raspberry Pi via the TCP/IP (Transmission Control Protocol/Internet Protocol) communication protocol. The sensors used for environmental sensing were implemented on the programmable interface controller and the data were stored in time sequence. A statistical analysis software platform was adopted for data preprocessing, modelling, and prediction to provide necessary maintenance decision. Using multivariate analysis users can obtain more information about the equipment's status, and the administrator can also determine the operational situation before unexpected device anomalies. The developed modules are decisively helpful in preventing unpredictable losses, thus improving the quality of services.
With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power ...of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.
Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules ...or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models.
This paper aims to investigate the feasibility of using system power consumption as a factor to improve laptop heat dissipation. The problems due to the CPU overheating are addressed. Based on the ...Taguchi method, the laptop fan parameters can be optimized with firmware adjustments only. In the Taguchi analysis, the fan speed, system power, and debounce time are considered as control factors, while the Cinebench point is utilized to evaluate the CPU performance. Experimental results demonstrate that the proposed heat dissipation scheme effectively reduces the idle time of a laptop fan. The improvement in heat dissipation can reduce CPU performance degradation because of overheating. According to the best combination of control factors, there is approximately a 5% increase in CPU performance despite a 0.35% increment in power consumption. This paper highlights the effectiveness of optimizing laptop fan parameters through firmware adjustments to improve heat dissipation and mitigate CPU overheating issues. Moreover, the study highlights the delicate balance between power consumption and performance gains. While there may be a slight increase in power consumption associated with the optimized heat dissipation scheme, the observed improvements in CPU performance outweigh this incremental power usage.
In recent years the increased rate of the aging population has become more serious. With aging, the elderly sometimes inevitably faces many problems which lead to slow walking, unstable or weak limbs ...and even fall-related injuries. So, it is very important to develop an assistive aid device. In this study, a fuzzy controller-based smart walker with a distributed robot operating system (ROS) framework is designed to assist in independent walking. The combination of Raspberry Pi and PIC microcontroller acts as the control kernel of the proposed device. In addition, the environmental information and user postures can be recognized with the integration of sensors. The sensing data include the road slope, velocity of the walker, and user's grip forces, etc. According to the sensing data, the fuzzy controller can produce an assistive force to make the walker moving more smoothly and safely. Apart from this, a mobile application (App) is designed that allows the user's guardian to view the current status of the smart walker as well as to track the user's location.
This paper mainly addresses the decentralized formation problems for multiple robots, where a fuzzy sliding-mode formation controller (FSMFC) is proposed. The directed networks of dynamic agents with ...external disturbances and system uncertainties are discussed in consensus problems. To perform a formation control and to guarantee system robustness, a novel formation algorithm combining the concepts of graph theory and fuzzy sliding-model control is presented. According to the communication topology, formation stability conditions can be determined so that an FSMFC can be derived. By Lyapunov stability theorem, not only the system stability can be guaranteed, but the desired formation pattern of a multirobot system can be also achieved. Simulation results are provided to demonstrate the effectiveness of the provided control scheme. Finally, an experimental setup for the e-puck multirobot system is built. Compared to first-order formation algorithm and fuzzy neural network formation algorithm, it shows that real-time experimental results empirically support the promising performance of desire.
This paper aims to establish a predictive model for battery lifetime using data analysis. The procedure of model establishment is illustrated in detail, including the data pre-processing, modeling, ...and prediction. The characteristics of lithium-ion batteries are introduced. In this study, data analysis is performed with MATLAB, and the open-source battery data are provided by NASA. The addressed models include the decision tree, nonlinear autoregression, recurrent neural network, and long short-term memory network. In the part of model training, the root-mean-square error, integral of the squared error, and integral of the absolute error are considered for the cost functions. Based on the defined health indicator, the remaining useful life of lithium-ion batteries can be predicted. The confidence interval can be used to describe the level of confidence for each prediction. According to the test results, the long short-term memory network provides the best performance among all addressed models.
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