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In recent years, with the steady and rapid growth of the national economy, various industries have developed rapidly and the resulting fire accidents have also been on the rise. This study mainly discusses the control system of a fire rescue robot for a high-rise building design. Robot fire detection method involves obtaining the video image of the fire detection area through a high-definition camera, performing image preprocessing on the current frame image of the video in the robot operating system to obtain the image; and performing the flame area segmentation based on the Ohta color space and the Otsu threshold segmentation algorithm on the image, to obtain the segmented image; through the interframe difference method, the preprocessed image of the previous frame is subtracted from the current frame image and the moving area of the image is segmented to obtain the segmented image; the obtained segmented image is combined with the segmented image in the robot operating system and images are intersected to obtain a segmented image with the characteristics of flame motion; based on other characteristics of the flame, flame recognition is performed on the area in the segmented image. Fires in high-rise buildings are increasing gradually, which seriously endangers the safety of human life and property. The firefighting robot and its control system play an important role in the fire protection of high-rise buildings. The purpose of this study is to analyze the disaster relief effect of a firefighting robot and control system in high-rise buildings. In this study, we use MATLAB software to model and simulate the rescue situation of a firefighting robot in high-rise buildings. The fuzzy control system established in the fuzzy logic toolbox of MATLAB can easily replace human field work and can change the control rules and membership function in the FIS editor. The results show that the rise time and adjustment time of the system are basically the same under the condition of variable load stiffness. The maximum overshoot is 0.59%, and the steady-state error is 0.19%. The maximum overshoot is 1.0148%, and the stability error is 0.46%. It is concluded that the expert PID algorithm is efficient and practical. It can be concluded that the robot position control system adopts PID control algorithm, and the attitude control system adopts expert PID control algorithm. This research provides some value for the development and design of firefighting robots in high-rise buildings in the future and also brings important significance. [ABSTRACT FROM AUTHOR]

The basic technological process of sludge conditioning and pressing is introduced in this paper. The purpose is to set up an adaptive fuzzy control algorithm to treat the problems of conditioning, mixing, feeding, and pressing control, so that the equipment used to process the sludge can run on demand. The algorithm is verified by MATLAB simulation. The results show that the control accuracy of the fuzzy adaptive control system is significantly improved compared with the original PID (proportion integration differentiation) control algorithm. The upper computer program is compiled with VB (visual basic) and applied to industry control to save the energy consumption. [ABSTRACT FROM AUTHOR]

The steering system, suspension system, and braking system of the vehicle are interrelated, so the ride comfort and handling stability of the vehicle are also closely related. But the vertical and lateral dynamics equations and controls system of the vehicle are always independent of each other, and the multiobjective control is generally achieved through the coordination of control algorithms. In this paper, taking the dynamic load of the tire as a link, the vertical dynamic model and the lateral dynamic model of heavy-duty vehicle are coupled. When the heavy-duty vehicle is turning, the proposed coupling model not only reflects the influence of the front wheel angle on the vertical motion and the vertical tire load, but also reflects the unevenness of the road surface on vehicle lateral motion. In order to improve the handling stability and transient safety of the vehicle, a synchronous control system combining six-wheel steering and front wheel active steering is proposed. It solves the problem that it is difficult to effectively track the desired yaw rate for the three-axle all-wheel steering vehicle with the middle rear wheel angle as the control input. Under the framework of the vehicle vertical/lateral unified coupling dynamics model, the semiactive suspension system controlled by fuzzy PID and the six-wheel active steering system combined with fuzzy control and fuzzy PID control are integrated. It is verified that the synchronous control method effectively optimizes the vertical and lateral motion characteristics of heavy-duty vehicles during steering and, at the same time, improves the ride comfort and steering stability. [ABSTRACT FROM AUTHOR]

Mechatronics production line is widely used in the production and operation of all walks of life, which greatly reduces the amount of labor of employees, improves the production level of enterprises, and creates great social and economic benefits for enterprises. The traditional manipulator system has modeling uncertainty and is vulnerable to external interference. In this paper, a compound control scheme is designed by combining the torque controller and fuzzy compensator, which effectively improves the system's response speed and greatly reduces the steady-state tracking error of the system. At the same time, the tracking process of the scheme is stable and flexible, and the chattering is minimal, which effectively improves the trajectory tracking performance of the system. Experiments show that the algorithm effectively solves the contradiction between computational complexity and control accuracy, improves the system's response speed, and reduces the system's steady-state tracking error. [ABSTRACT FROM AUTHOR]

A mature coordinated and optimized control system can bring timeliness and benefits to the production line. This paper firstly exemplifies the big data intelligent production line coordination control system model and algorithm. They, respectively, include the intelligent production line coordination control system model, the big data intelligent coordination control model, and the hardware key electrical control system model of the air conditioning assembly big data intelligent production line, taking the tea production line as an example. The parameters of the coordinated control model are identified by the algorithm and system design, which considers and adjusts the functional requirements of the system to maintain the stable operation of the software system and store and transmit efficiently. To achieve efficient fit between functional requirements and modules, it created a complete assembly automation production line that meets actual needs. Then, the experiment of big data intelligent coordination and optimization control system based on cement clinker production line is carried out, and the intelligent coordination control system of cement clinker is designed. Compared with the data, it can be seen that in the coordinated control mode, the fluctuation of the temperature and pressure of the main steam is smaller, which can make the entire power generation system operate safely and stably. Based on the fuzzy control of the waste heat boiler and steam turbine load coordination control requirements, the reasonable coordination control of the boiler system and the generator set is carried out through fuzzy control, and the optimization and adjustment are carried out. Finally, the comparison and analysis of fuzzy control and traditional control experiments are carried out, and a coordinated optimization system using fuzzy control is obtained. It requires less feeding material, consumes less energy in the production process, generates more power, and can increase the average income by 4.87%, which has a good practical application prospect. [ABSTRACT FROM AUTHOR]

An information-based and intelligent comprehensive energy consumption management system should not only collect, manage, and analyze energy consumption data but also monitor the environmental data inside the building, to manage and display the real energy consumption and internal environmental quality more effectively and reliably. In order to solve this problem, the design and construction of the front-end hardware of the energy consumption management system are proposed, mainly including the design of data acquisition node and data centralization end. The design of data acquisition node includes the selection of DSP controller, wireless communication module, and data acquisition module and the design of main circuit and peripheral circuits. The design of data centralization end includes the selection of its high-performance processor and the transplantation of Linux operating system. This paper only introduces the design and research of lighting control algorithm and applies fuzzy control algorithm to intelligent lighting. The fuzzy control system is introduced, and a fuzzy controller based on intelligent lighting is created. The fuzzy control rule table and fuzzy control query table of the intelligent lighting system are obtained through the fuzzy control algorithm. According to the basic rules of light intensity control, when the indoor light intensity is greater than the set value of light intensity, the LED lamps are turned off, so when e < 0 , the LED lamp is off and the PWM output value is 0. Therefore, when designing the fuzzy controller of intelligent lighting system, the basic domain of illumination intensity error E is 0500 , excluding the case that the indoor illumination intensity is greater than the set value of illumination intensity. [ABSTRACT FROM AUTHOR]

This paper investigates the problems of the robust fault estimation (FE) and fault-tolerant control (FTC) for the Takagi-Sugeno (T-S) fuzzy systems with unmeasurable premise variables (PVs) subject to external disturbances, actuator, and sensor faults. An adaptive fuzzy sliding mode observer (SMO) with estimated PVs is designed to reconstruct the state, actuator, and sensor faults simultaneously. Compared with the existing results, the proposed observer is with a wider application range since it does not require the knowledge of the upper bound of faults that some FE methods demand. Based on the FE information, a dynamic output-feedback fault-tolerant controller (DOFFTC) is designed to compensate the effect of faults by stabilizing the closed-loop systems. By using the H ∞ filtering method, sufficient conditions for the existence of the proposed SMO and DOFFTC are derived in terms of linear matrix inequalities (LMIs) optimization. Finally, a nonlinear inverted pendulum system is given to validate the proposed methods. [ABSTRACT FROM AUTHOR]

Driver drowsiness is a severe problem that usually causes traffic accidents, classified as more dangerous. The record of the National Safety Council reported that drowsy driving is caused by 9.5% of all crashes (100,000 cases). Therefore, preventing and minimizing driver fatigue is a significant research area. This study aims to design a nonintrusive real-time drowsiness system based on image processing and fuzzy logic techniques. It is an enhanced approach for Viola–Jones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth features to detect driver drowsiness based on the desired facial feature image in a specific driver video frame. The size and orientation of the captured features were tracked and handled for determining image features such as brightness, shadows, and clearness. Lastly, the fuzzy control system provides different alert sounds based on the tracked information from the face, eyes, and mouth in separate cases, such as race, wearing glasses or not, gender, and various illumination backgrounds. The experiments' results show that the proposed approach achieved high accuracy of 94.5% in detecting driver status compared with other studies. Also, the fuzzy logic controller efficiently issued the required alert signal of the drowsy driver status that helps to save the driver's life. [ABSTRACT FROM AUTHOR]

In view of the low efficiency in traditional palletizing robot problem of poor control precision, this paper introduces fuzzy PID position control algorithm, based on the actual operation situation of palletizing robot; determined as palletizing robot FPGA hardware platform, hardware platform based on this fuzzy PID position control algorithm is applied to implement palletizing robot motion control system design. The simulation model of fuzzy PID motion control was established by MATLAB software for testing to determine that the fuzzy PID position control algorithm reflects the time quickly in the motion control of palletizing robot, and the actual overshooting is small, which is more suitable for the motion control algorithm of palletizing robot. Under this condition, the modular method is adopted to complete the system application design on the FPGA hardware platform. [ABSTRACT FROM AUTHOR]

This study provides a new embedded linear model (ELM) three-dimensional fuzzy PID control method for a bionic autonomous underwater vehicle (AUV), which is disturbed by ocean waves. The uncertainty of the ocean waves will make it difficult for the AUV to achieve good sailing quality, especially near the sea surface. In view of this problem, an ELM fuzzy PID controller, which adopts three-dimensional fuzzy control rules, is designed. In the ELM fuzzy PID controller, a linear model of AUV is established in order to actively perceive the effect of waves on AUV. Hence, the wave disturbance can be reflected in the control signal earlier and faster. Finally, three simulations are carried out to verify the control effect of the ELM fuzzy PID controller. An ocean wave disturbance model is established in order to simulate the force and the moment acting on the AUV. The results imply that compared with the classical PID controller and normal fuzzy PID controller, the ELM fuzzy PID controller can significantly enhance the stability of AUV and make the response faster and overshoot smaller. [ABSTRACT FROM AUTHOR]

An intelligent controller based on a self-learning interval type-II fuzzy neural network is proposed to make the motion controller of the industrial intelligent robot with good adaptability. This controller has a parallel structure and contains an interval type-II fuzzy neural network and a conventional PD controller. For the design of the interval type-II fuzzy neural network, the interval type-II fuzzy set is established using the slave design method. In the design process of the interval type-II fuzzy set of the front piece, a dual sequence symmetric trapezoidal subordinate function arrangement method is proposed, which makes the self-learning law and stability analysis of the system in an analytic form and facilitates the implementation of the algorithm in hardware. In the design of the neural network self-learning law, a parametric self-learning algorithm based on sliding mode control theory is established to adjust the structural parameters of the interval type-II fuzzy neural network online, and the stability of the system is proved by using Lyapunov's stability theorem. Three sets of validation simulation experiments are given in conjunction with the trajectory tracking problem of the Delta parallel robot. The simulation results show that, in the presence of system uncertainty, the intelligent controller based on interval self-learning interval type-II fuzzy neural network can significantly improve the trajectory tracking accuracy and robustness of the system and make the control system highly adaptable to the environment. Experiments of intelligent control system based on self-learning interval type-II fuzzy neural network and experiments of reusable particle swarm optimal motion planning method are designed, and the effectiveness of the intelligent control system and motion planning method is verified on the experimental platform. The experimental results show that the intelligent control system based on the self-learning interval type-II fuzzy neural network can effectively improve the accuracy and stability of robot trajectory tracking control, and the reusable particle swarm optimal motion planning method can quickly solve the robot motion planning problem with complex constraints online. [ABSTRACT FROM AUTHOR]

In this paper, a self-adaptive, two-stage fuzzy controller is established to realize the real-time online optimization of traffic signal timing plan, which takes multimodels of transportation as the research object to analyze the reliability of the control system at the isolated urban intersection. In this system, the first stage calculates traffic urgency degree for all red phases and selects the red phase with maximum traffic urgency degree as the next green phase. The second stage determines whether to extend or terminate the current signal phase. Aiming at the problems of the parameters of membership functions empirical settings and insufficient response to the real-time fluctuation in traffic flow, the controller introduces an improved hybrid genetic algorithm to solve it and enable the controller to self-learn. Finally, a microsimulation platform is constructed based on the VISSIM and Python language to evaluate the efficiency and reliability of the controller under complex actual traffic conditions. Results showed that the average delay time per vehicle is reduced by 14.59%, while the average number of stops per vehicle is reduced by 0.71% compared with the traditional control method. Results indicate that the traffic signal timing plan generated by the controller can efficiently improve the intersection traffic capacity and has good efficiency and reliability under the condition of medium saturation and unsteady flow. [ABSTRACT FROM AUTHOR]

In order to improve the intelligence of the e-commerce online intelligent customer service system, this paper proposes a deep rejection recognition algorithm based on the maximum interval squared hinge loss and combines the actual needs of the e-commerce online customer service system to build an intelligent customer service system with the support of the fuzzy control system. Moreover, this article chooses to build a domain ontology library for structured storage of domain knowledge needed by customer service chatbots. In addition, this article analyzes the dialogue structure based on the speech act model and combines the semantic vector model of the question sentence on the basis of the dialogue structure to understand the question sentence, which helps to improve the accuracy of the answer feedback of the Internet shopping customer service robot. Finally, this article designs experiments to verify the performance of the online customer service system constructed in this article and analyzes the experimental results through statistical methods. The experimental results show that the online intelligent customer service system constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

The rapid development of intelligent control technology has improved the functions of service robots oriented to the home environment, and the functional requirements of family members for service robots have also been upgraded from simply liberating hands and reducing housework to emotional communication and intelligent escort. Based on the Internet of Things and fuzzy control technology, this paper builds a home robot control system and gives a brief overview around the mechanical structure design of the home service robot, mainly focusing on the core control system and global path planning methods. Moreover, this paper adopts the control system structure that combines the upper computer and the bottom motion controller and combines it with simple and practical system software, so the system stability is high. Finally, this paper verifies the performance of the system constructed in this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

This paper investigates the control problem of the buoyancy regulation system for autonomous underwater vehicle (AUV). There are some problems to be considered in the oil-water conversion-based buoyancy regulation system, including the external seawater pressure, the pressure fluctuations, and the slow switching speed of the ball valve. The control accuracy of the buoyancy regulation under the traditional PID controller cannot meet the requirements of the project. In this paper, a fuzzy sliding mode control scheme is developed for the buoyancy regulation system to solve the abovementioned problems. At first, a mathematical model of the buoyancy regulation system is established, and the stability of the system is analyzed. Then, the sliding mode control algorithm is combined with the fuzzy system to improve the control accuracy. Finally, the pool-experiment results on a prototype show that the developed control scheme can meet the requirements of the control accuracy for the buoyancy regulation system. [ABSTRACT FROM AUTHOR]

This paper presents the development of a virtual didactic tool for students of mechatronic engineering taking an intelligent system course. The objective of the tool is for students to learn the structures for fuzzy control systems. This tool makes it easier for students to understand the behavior of the membership functions of input and output variables, the evaluation of the set of fuzzy rules, and the method of defuzzification, giving the students the possibility of applying a fuzzy controller in industrial processes using a data acquisition board. The proposed tool was developed with the virtual instrumentation software LabVIEW. It has the advantage that students can manipulate the internal structure of the fuzzy logic control system in a unique window where students can analyze the behavior of internal signals by looking at the response graphs. The fuzzy controller can be easily translated to a real application by using LabVIEW compatible hardware. To have feedback from students on the use of the tool and to understand if this tool allows an improvement in their academic performance, a 2-hour workshop on the proposed application was given to a group of 93 students. At the end of the workshop, a knowledge assessment and a perception survey were applied to the participants. The academic performance achieved by students who were given the workshop using the proposed teaching tool was compared with the academic performance of students who witnessed the workshop using Matlab tools. The statistical analysis of the results obtained for the knowledge assessment shows that the students that had taken the workshop using the proposed teaching tool had better compression of the topic compared to the students that had taken the workshop using the Fuzzy Logic Toolbox provided by Matlab MathWorks. The students that had taken the workshop using the proposed teaching tool obtained a mean grade of 89.63/100, while students that had taken the workshop using Matlab's tools obtained a mean grade of 69.85/100. Also, the students' perception of the proposed tool was that it allowed the design of fuzzy control systems in a simple and intuitive way. [ABSTRACT FROM AUTHOR]

Aiming at the challenging problem of the traditional warp knitting machine electronic jacquard control system with complex structure of multiple circuit boards layered cascade, such as large physical space occupation, high power consumption, and independent high-voltage power supply voltage, we proposed an embedded circuit and control strategy design for the piezoelectric jacquard needle (PJN) with adaptive boost and energy recovery functions. Firstly, the electromechanical dynamics model of PJN was established. Secondly, the fuzzy PI double closed-loop control algorithm driven by a finite state machine is proposed. Thirdly, with the help of a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET), the PJN is integrated with the drive circuit. The drive circuit of PJN uses an energy storage inductor to replace the current limiting resistor of the traditional drive circuit, which can not only limit the forward charging current of the PJN and reduce energy loss but also can use the energy absorbed from the low-voltage power supply to adaptively boost the power supply of the PJN to the high voltage required for working conditions. The simulation results show that the new PJN drive circuit has an adaptive self-boost function. The PWM signal modulated by the fuzzy PI double closed-loop control algorithm can efficiently and accurately control the adaptive boost power supply and the voltage across the PJN. The mode of the circuit can be correctly switched through the sequential logic of the finite state machine and realize the energy recovery function. [ABSTRACT FROM AUTHOR]

High-pressure fuel line is a basic component of many modern industrial devices, and its pressure stability affects operating efficiency and safety. In this paper, the working mechanism of high-pressure fuel line with double fuel injectors and pressure reducing valve is studied as the main object, the differential equation for changes in pressure with time is established, and the rotating speed of high-pressure fuel pump cam, fuel injection time of fuel injector, and the threshold of pressure reducing valve are optimized using fuzzy logic systems to control the pressure in high-pressure fuel line within a safe range and improve the operating efficiency and safety of high-pressure fuel line. The results of numerical simulation show that the pressure reducing valve can effectively control the pressure in high-pressure fuel line. [ABSTRACT FROM AUTHOR]

Soil compaction is an important procedure of precision seeding operation. In this paper, a precise downforce control system based on fuzzy PID was proposed in order to improve the quality of the soil compaction and the accuracy of setting working parameters. The conventional mechanism of seeders for soil compaction was optimised. The compressing spring of the compaction mechanism was replaced by a linear motor, which is actively controlled to adjust downforce in real time. A force sensor was connected in series with the linear motor to detect the actual downforce from a press wheel acting on soil. The detected downforce was employed as feedback for the fuzzy PID model. A slave real-time control system was constructed by using an STM32 microcontroller. A user interface was designed for the portable master computer system based on the ForLinx embedded platform to facilitate the setting of target downforce and display the actual downforce in real time. Meanwhile, it was able to adjust the system for different operating requirements, such as soil stiffness, moisture, and crop species. Experiments were conducted on a soil bin, and the results indicated that the active control system has better performance than the conventional passive system in downforce control. The downforce was stable with a variance less than 2.6% under different conditions, and it was 8.11% less than the conventional passive system. [ABSTRACT FROM AUTHOR]

This paper studies the stabilization design scheme with H ∞ performance for a large class of nonlinear discrete-time systems. The system under study is modeled by Takagi-Sugeno (T-S) model with local nonlinearity and state delay. First, the model is changed into an equivalent fuzzy switching model. And then, according to projection theorem and piecewise Lyapunov function (PLF), two new H ∞ control methods are proposed for fuzzy switched systems, which consider the time delay information of the system. Finally, the relationship among all fuzzy subsystems is considered. Because the results are only expressed by a series of linear matrix inequalities (LMIs), the controller can be directly designed by the linear matrix inequalities toolbox of MATLAB. [ABSTRACT FROM AUTHOR]

Cloud computing can enable organizations to do more by breaking the physical bonds between an IT foundation. The raised security dangers in cloud computing must be overpowered to profit the new processing perspective that offers an imaginative arrangement of activity for relationship to IT. The purpose of the study was to reduce security's obstacles and risks by using protection methods and approaches to ensure maximum data protection, which allows for the user to select the original security level. An adaptive neural control fuzzy system is used to resolve the unsecure and risky tasks of cloud computing. Sugeno control methods have been applied for these data protection issues in which the uncertainty because of randomness can be resolved. ANFIS identified the input parameters according to the current scenario, fuzzified the data, and integrated them into knowledge rule base. Different membership functions were used for training the data. In this article, we present a point-by-point examination of the cloud security issue. We assessed the issue from the cloud building point of view. In context of this examination, we deduce an unmistakable detail of the cloud security issue and key highlights that ought to be confirmed by any proposed security strategy. The examination and results show that the parameters dependent on ANFIS are very much intended to distinguish the oddities in cloud condition with least bogus negative rate and high discovery precision. The performance of Sugeno membership function usually gives better results and ensures the computational efficiency and accuracy of data. [ABSTRACT FROM AUTHOR]

All persons in self-driving vehicle would like to receive each service. To do it, the system has to know the person's state from emotion or stress, and to know the person's state, it has to catch by analyzing the person's bio-information. In this paper, we propose a system for inferring emotion using EEG, pulse, blood pressure (systolic and diastolic blood pressure) of user, and recommending color and music according to emotional state of user for a user service in self-driving vehicle. The proposed system is designed to classify the four emotional information (stability, relaxation, tension, and excitement) by using EEG data to infer and classify emotional state according to user's stress. SVM algorithm was used to classify bio information according to stress index using brain wave data of the fuzzy control system, pulse, and blood pressure data. When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. The bio-information classification system based on the stress index proposed in this paper will help to study the interaction between human and computer (HCI) in the 4th Industrial Revolution by classifying emotional color and emotional sound according to the emotion of the user it is expected. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stabilization for semi-Markov chaotic systems with fuzzy sampled-data controllers, in which the semi-Markov jump has generally uncertain transition rates. The exponential stability condition is ffirstly obtained by the following two main techniques: To make full use of the information about the actual sampling pattern, a novel augmented input-delay-dependent Lyapunov-Krasovskii functional (LKF) is firstly introduced. Meanwhile, a new zero-value equation is established to increase the combinations of component vectors of the resulting vector. fie corresponding fuzzy sampled-data controllers are designed based on the stability condition. Finally, the validity and merits of the developed theories are shown by two numerical examples. [ABSTRACT FROM AUTHOR]

Urban rail transit has played an important role in big and crowded cities. Providing services with high levels of customer satisfaction is essential in order to increase the sharing rate of urban rail transit and to reduce traffic congestion by shifting people away from private car use. Therefore, it is of great significance to improve the customer satisfaction of urban rail transit. This paper presents an intuitionistic fuzzy group decision model to evaluate the customer satisfaction of urban rail transit. An evaluation indicator system including seven categories of indicators is established to measure passengers' satisfaction. The overall customer satisfaction level of the urban rail transit lines is obtained by the intuitionistic fuzzy entropy and intuitionistic fuzzy weighted average (IFWA) operator. The intuitionistic fuzzy entropy is used to solve attribute weights and IFWA operator is used to solve the information aggregation. Drawing on Tianjin urban rail transit lines as a case study, the detailed analyses were conducted to evaluate the overall customer satisfaction level of five urban rail transit lines and as such suggesting remedy strategies. The results can help urban rail transit operation company to improve the service quality of urban rail transit. [ABSTRACT FROM AUTHOR]

Cloud computing can be considered as one of the leading-edge technological advances in the current IT industry. Cloud computing or simply cloud is attributed to the Service Oriented Architecture. Every organization is trying to utilize the benefit of cloud not only to reduce the cost overhead in infrastructure, network, hardware, software, etc., but also to provide seamless service to end users with the benefit of scalability. The concept of multitenancy assists cloud service providers to leverage the costs by providing services to multiple users/companies at the same time via shared resource. There are several cloud service providers currently in the market and they are rapidly changing and reorienting themselves as per market demand. In order to gain market share, the cloud service providers are trying to provide the latest technology to end users/customers with the reduction of costs. In such scenario, it becomes extremely difficult for cloud customers to select the best service provider as per their requirement. It is also becoming difficult to decide upon the deployment model to choose among the existing ones. The deployment models are suitable for different companies. There exist divergent criteria for different deployment models which are not tailor made for an organization. As a cloud customer, it is difficult to decide on the model and determine the appropriate service provider. The multicriteria decision making method is applied to find out the best suitable service provider among the top existing four companies and choose the deployment model as per requirement. [ABSTRACT FROM AUTHOR]

Layout flexibility is critical for the performance of flexible manufacturing cells, especially in dynamic production environment. To improve layout flexibility, layout optimization should consider more flexible factors based on existed models. On the one hand, not only should the current production demands be covered, but also the future uncertain demands should be considered so that the cell can adapt to the dynamic changes in a long term. On the other hand, the flexibility of machines should be balanced in the layout in order to guarantee that the cell can deal with dynamic new product introduction. Starting from these two points, we formulate a layout optimization model based on fuzzy demand and machine flexibility and then develop a genetic algorithm with bilayer chromosome to solve the model. We apply this new model to a flexible cell of shell products and test its performance by comparing it with the classical two-stage model. The total logistics path of the new model is shown to be significantly shorter than the classical model. Then we carry out adaptability experiments to test the flexibility of the new model. For the dynamic situation of both the fluctuation of production demands and the introduction of new products, the new model shows obvious advantages to the classical model. The results indicate that this advantage becomes greater as the dynamics becomes greater, which implies that considering fuzzy demand and machine flexibility is necessary and reasonable in layout optimization, especially when the dynamics of the production environment is dramatic. [ABSTRACT FROM AUTHOR]

This paper studies fuzzy controller design problem for a class of nonlinear switched distributed parameter systems (DPSs) subject to time-varying delay. Initially, the original nonlinear DPSs are accurately described by Takagi-Sugeno fuzzy model in a local region. On the basis of parallel distributed compensation technique, mode-dependent fuzzy proportional and fuzzy proportional-spatial-derivative controllers are constructed, respectively. Subsequently, using single Lyapunov-Krasovskii functional and some matrix inequality methods, sufficient conditions that guarantee the stability and dissipativity of the closed-loop systems are presented in the form of linear matrix inequalities, which allow the control gain matrices to be easily obtained. Finally, numerical examples are provided to demonstrate the validity of the designed controllers. [ABSTRACT FROM AUTHOR]

A robust adaptive H∞ tracking control design for nonlinear stochastic systems with both Brownian motion and Poisson jumps is proposed, which is based on Takagi–Sugeno (T-S) type fuzzy techniques. Because the state of to-be-controlled systems cannot be known exactly, to overcome this difficulty, the state estimation systems and error estimation systems are introduced to obtain an augmented system. By using the fuzzy systems to approximate the nonlinear systems, an adaptive fuzzy control is employed to achieve the desired H∞ tracking performance for stochastic systems with exogenous disturbance. A simulation example is presented to illustrate the tracking performance of the proposed design method. [ABSTRACT FROM AUTHOR]

This paper proposes an observer-based fuzzy control scheme for a class of memristive chaotic circuit systems. First, the Takagi-Sugeno fuzzy model is adopted to reconstruct the nonlinear chaotic circuit system. Next, based on the proposed fuzzy model, an observer-based fuzzy controller is developed to estimate the states and stabilize the origin. Third, the results are extended to explore the L∞-gain observer-based fuzzy control for the chaotic system with disturbances. Finally, simulation results are also addressed to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, an effective method based on Transform Invariant Low-rank Textures (TILT) and HOG is proposed to identify woven fabric pattern. Firstly, the method based on TILT is used to solve the deflection phenomenon in the process of woven fabric image acquisition. Secondly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the 2D spatial-domain gray projection, which is used to segment the weft and warp yarns. Thirdly, HOG is applied to extract distinctive invariant features in the process of feature extraction. According to the HOG feature, the texture features of the woven fabric are acquired. Finally, the yarn floats are classified by Fuzzy C-Means (FCM) clustering to recognize the weft and warp cross. Experimental results demonstrate that the proposed method can achieve the recognition of the three woven fabrics, plain, twill, and satin, and obtain accurate classification results. [ABSTRACT FROM AUTHOR]

Aiming at the vibrations of the multistage gear transmission system aroused by the gear meshing excitation, a novel active vibration control structure with built-in piezoelectric actuators (PZT) was established. The active control forces generated by the PZT were transmitted to the shafts through the additional supporting bearings. In addition, an adaptive fuzzy proportion integration differentiation (AFPID) control algorithm was proposed as the primary control algorithm to reduce the transverse vibrations of the gear shaft. According to the control law of PID parameters, a fuzzy inference module was designed to adaptively adjust the PID parameters to obtain the optimal control effect. An experimental platform was set up to verify the control effect of the algorithm. The experiments performed show 10 dB reduction in housing vibrations at certain targeted mesh harmonics over a range of operating speeds. [ABSTRACT FROM AUTHOR]

Risk assessment for tunnel portals in the construction stage has been widely recognized as one of the most critical phases in tunnel construction as it easily causes accident than the overall length of a tunnel. However, the risk in tunnel portal construction is complicated and uncertain which has made such a neural network very attractive to the construction projects. This paper presents a risk evaluation model, which is obtained from historical data of 50 tunnels, by combining the fuzzy method and BP neural network. The proposed model is used for the risk assessment of the Tiefodian tunnel. The results show that the risk evaluation level is IV, slope instability is the greatest impact index among four risk events, and the major risk factors are confirmed. According to the evaluation results, corresponding risk control measures are suggested and implemented. Finally, numerical simulation is carried out before and after the implementation of risk measures, respectively. The rationality of the proposed risk evaluation model is proved by comparing the numerical simulation results. [ABSTRACT FROM AUTHOR]

An improved fuzzy neural network (FNN)/proportion integration differentiation (PID) compound control scheme based on variable universe and back-propagation (BP) algorithms is proposed to improve the ability of disturbance rejection of a three-axis inertially stabilized platform (ISP) for aerial remote sensing applications. In the design of improved FNN/PID compound controller, the variable universe method is firstly used for the design of the fuzzy/PID compound controller; then, the BP algorithm is utilized to finely tune the controller parameters online. In this way, the desired performances with good ability of disturbance rejection and high stabilization accuracy are obtained for the aerial ISP. The simulations and experiments are, respectively, carried out to validate the improved FNN/PID compound control method. The results show that the improved FNN/PID compound control scheme has the excellent capability in disturbance rejection, by which the ISP’s stabilization accuracy under dynamic disturbance is improved significantly. [ABSTRACT FROM AUTHOR]

In this paper, two main contributions are presented to manage the power flow between a wind turbine and a solar power system. The first one is to use the fuzzy logic controller as an objective to find the maximum power point tracking, applied to a hybrid wind-solar system, at fixed atmospheric conditions. The second one is to respond to real-time control system constraints and to improve the generating system performance. For this, a hardware implementation of the proposed algorithm is performed using the Xilinx System Generator. The experimental simulation results show that the suggested system presents high accuracy and acceptable execution time performances. The proposed model and its control strategy offer a proper tool for optimizing the hybrid power system performance which we can use in smart house applications. [ABSTRACT FROM AUTHOR]

A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem. [ABSTRACT FROM AUTHOR]

Multifrequency controlled synchronization of two homodromy eccentric rotors is investigated. The vibrating screen with two rotors is a typical underactuated system. So, the model of the vibrating screen is converted to a synchronization motion problem. Firstly, the mechanical-electromagnetic coupling dynamical model of the vibrating system is established. And then the fuzzy PID method is used for the two motors which are based on the master-slave control strategy. The slave motor uses the method of the phase ratio to trace the master motor and achieve the synchronous motion. Through the simulation, the two rotors cannot reach the state of multifrequency self-synchronization. And, then, it is realized by multifrequency controlled synchronization. The article presents the motion trails of the vibrating system between one and two times and realizes the zero phase difference after each period. Finally, some experiments are used to verify the accuracy and effectiveness of the conclusion in the simulation and analysis of the feature of the movement tracks. [ABSTRACT FROM AUTHOR]

A class of quaternion-valued fuzzy cellular neural networks with time-varying delays on time scales is proposed. Based on inequality analysis techniques on time scales, a fixed point theorem and the theory of calculus on time scales, the existence, and global exponential stability of anti-periodic solutions for this class of neural networks are established. The obtained results are completely new and supplement to the known results. Finally, a numerical example is given to illustrate the feasibility of our results. [ABSTRACT FROM AUTHOR]

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems. [ABSTRACT FROM AUTHOR]

We suggested different structured hybrid systems for the sentence-level subjectivity analysis based on three supervised machine learning algorithms, namely, Hidden Markov Model, Fuzzy Control System, and Adaptive Neuro-Fuzzy Inference System. The suggested feature extraction algorithm in our experiment computes a feature vector using statistical textual terms frequencies in a training dataset not having the use of any lexical knowledge except tokenization. Taking into consideration this fact, the above-mentioned methods may be employed in other languages as these methods do not utilize the morphological, syntactical, and lexical analysis in the classification problems. [ABSTRACT FROM AUTHOR]

Considering the existence of many actual influential factors of a high voltage switch cabinet in the coal mine, the paper proposes a hierarchical fuzzy comprehensive evaluation algorithm to assess the running state of the 6 kV (10 kV) power vacuum switch cabinet better and improve operational safety and reliability of the power supply system. The algorithm can realize the state evaluation of bus bars, vacuum circuit breakers, electrical cables, other components, and the switch cabinet by the bottom-up approach. The running state evaluation index system and mathematical model of the switchgear are introduced particularly. According to the different forms of data information, this algorithm comprehensively evaluates the running state of the high voltage switchgear by using the online running data, test data, equipment information, and historical data records of the distribution cabinet. The actual operation states and test data of a handcart switch cabinet are taken as examples to prove the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved. [ABSTRACT FROM AUTHOR]

A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example. [ABSTRACT FROM AUTHOR]

This paper designs a kind of adaptive fuzzy controller for robotic manipulator considering external disturbances and modeling errors. First, n-link uncertain robotic manipulator dynamics based on the Lagrange equation is changed into a two-order multiple-input multiple-output (MIMO) system via feedback technique. Then, an adaptive fuzzy logic control scheme is studied by using sliding theory, which adopts the adaptive fuzzy logic systems to estimate the uncertainties and employs a filtered error to make up for the approximation errors, hence enhancing the robust performance of robotic manipulator system uncertainties. It is proved that the tracking errors converge into zero asymptotically by using Lyapunov stability theory. Last, we take a two-link rigid robotic manipulator as an example and give its simulations. Compared with the existing results in the literature, the proposed controller shows higher precision and stronger robustness. [ABSTRACT FROM AUTHOR]

Intelligent robotic vehicles are more and more fully automated, without steering wheels, gas/brake pedals, or gearshifts. However, allowing the human driver to step in and maneuver the robotic vehicle under specific driving requirements is a necessary issue that should be considered. To this end, we propose a wearable-sensing-based hands-free maneuver intention understanding approach to assist the human to naturally operate the robotic vehicle without physical contact. The human intentions are interpreted and modeled based on the fuzzy control using the forearm postures and muscle activities information detected by a wearable sensory system, which incorporates electromyography (EMG) sensors and inertial measurement unit (IMU). Based on the maneuver intention understanding model, the human can flexibly, intuitively, and conveniently control diverse vehicle maneuvers only using his intention expressions. This approach was implemented by a series of experiments in the practical situations on a lab-based 1/10 robotic vehicle research platform. Experimental results and evaluations demonstrated that, by taking advantage of the nonphysical contact and natural handleability of this approach, the robotic vehicle was successfully and effectively maneuvered to finish the driving tasks with considerable accuracy and robustness in human-robotic vehicle interaction. [ABSTRACT FROM AUTHOR]

Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions). USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings—not regulated by semaphores—which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system’s intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT. [ABSTRACT FROM AUTHOR]

This study presents the development and application of a fuzzy control system (FCS) for the control of the charge and discharge process for a bank of batteries connected to a DC microgrid (DC-MG). The DC-MG runs on a maximum power of 1 kW with a 190 V DC bus using two photovoltaic systems of 0.6 kW each, a 1 kW bidirectional DC-AC converter to interconnect the DC-MG with the grid, a bank of 115 Ah to 120 V lead-acid batteries, and a general management system used to define the operating status of the FCS. This FCS uses a multiplexed fuzzy controller, normalizing the controller’s inputs and outputs in each operating status. The design of the fuzzy controller is based on a Mamdani inference system with AND-type fuzzy rules. The input and output variables have two trapezoidal membership functions and three triangular membership functions. LabVIEW and the NI myRIO-1900 embedded design device were used to implement the FCS. Results show the stability of the DC bus of the microgrid when the bank of batteries is in the charging and discharging process, with the bus stabilized in a range of 190 V ± 5%, thus demonstrating short response times to perturbations considering the microgrid’s response dynamics. [ABSTRACT FROM AUTHOR]

Air traffic management is carried out by air traffic controllers assisted by complex technical systems that provide them with visualization of the traffic situation. In practice, visualization systems errors sometimes occur. The purpose of this paper is to determine the impact of errors of different types on the safety of the air traffic. The assessment of the threat level is influenced by subjective factors and cannot be expressed precisely. Therefore, the fuzzy reasoning theory has been used. The developed fuzzy model has been used to obtain a tool for simulation of the impact of various factors on traffic safety assessment. The results obtained indicate that the most important determinants of safety are the time when the air traffic controller remains unaware of the breakdown and the total time he/she does not have full knowledge of the traffic situation. It has been found that the key role for the proper operation of the air traffic visualization system and the restoration of full situational awareness is played by self-diagnostic systems that can restore the system’s correct functioning without even the controller being aware of the error occurrence. Their role in ensuring safety might be even greater than redundancy which is commonly used. [ABSTRACT FROM AUTHOR]

This article proposes a comparative method to assess the performance of artificial neural network’s direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method. [ABSTRACT FROM AUTHOR]

Supply chain network is more complex and dynamic under the uncertain demand and the lead time. Robustness is a key index of the stable operation for the supply chain network. We investigate a fuzzy robust strategy to realize the robust operation of the supply chain network with the production lead times and the ordering lead times under the uncertain customer demand. A discrete switched model of the dynamic supply chain network with the lead times and the uncertain customer demand is established based on T-S fuzzy systems. Then a fuzzy switched strategy is proposed to control the switching actions among subsystems. Furthermore, by introducing the inhibition rate γ, a fuzzy control strategy for the dynamic supply chain network is put forward to suppress the impacts of the lead times and the uncertain customer demand on the operation of the dynamic supply chain network. The fuzzy robust strategy composed of the fuzzy switched strategy and the fuzzy control strategy can guarantee the robust operation of the supply chain network at low cost. Finally, the simulation researches show the advantage of the proposed fuzzy robust strategy through the comparisons with the common robust strategy. [ABSTRACT FROM AUTHOR]

The tracking control of H∞ dynamic output feedback is proposed for the fuzzy networked systems of the same category, in which each system is discrete-time nonlinear and is missing measurable data. In other words, the loss of data packet occurs randomly in both the uplink and the downlink. The independent variables that are called the Bernoulli random variables are considered to design the loss of data packets. The method of parallel distributed compensation (PDC) in terms of the T-S fuzzy model is applied to investigate the dynamic controller of tracking control on the systems. Then, it is presented that the analytical H∞ performance of the output error between the reference model and the fuzzy model for the closed-loop system containing dynamic output feedback controller is proven. Furthermore, the achieved sufficient conditions in terms of LMIs ensure that the closed-loop system is stochastically stable in the H∞ sense. Finally, a numerical system is offered to show the effectiveness of the established technique. [ABSTRACT FROM AUTHOR]