When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power refer...When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.展开更多
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t...In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.展开更多
An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and c...An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.展开更多
The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant...The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.展开更多
This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative ...This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.展开更多
The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are m...The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.展开更多
为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。...为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。展开更多
This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy sto...This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.展开更多
Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high rippl...Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high ripple torque is obtained. In order to reduce this ripple, a control strategy with modified current shapes is proposed. A workbench consisting of a machine prototype and the control system based on a microcontroller was built. These controllers were: a conventional PID, a fuzzy logic PID and a neural PID type. From experimental results, the effective reduction of the torque ripple was confirmed and the performance of the controllers was compared.展开更多
With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system ...With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system has always been the focus of research. Automatic parking modules can greatly assist drivers in parking operations, greatly reduce parking difficulties and make people more convenient and fast parking. In this paper, an automatic parking system based on the fuzzy controller is proposed. The fuzzy controller of automatic parking system is constructed by using fuzzy theory, and the robustness of the whole system is examined by fuzzy logic. Firstly, the vehicle motion rules and trajectory changes are analyzed in detail, and the real parking lot model is simulated. Then, the input and output variables of the whole system are analyzed by fuzzy theory and the membership function is constructed. Based on the experience of human experts, the parking rules are tested and summarized, and a reasonable and practical rule base is established. Finally, MATLAB is used to code, build the visual interface of parking lot and vehicles, and draw the cyclic iterative function to detect the vehicle position and direction angle, so as to act as a sensor. The results show that using a fuzzy controller to construct an automatic parking system can effectively improve the parking level.展开更多
In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuz...In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.展开更多
The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of o...The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization.In this paper,the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object.Fuzzy control was used to optimize the loading path,and the fuzzy rule base was established based on FEM.The minimum wall thickness and wall thickness reduction rate were determined as input membership functions,and the axial feeds variable value of the next step was used as output membership functions.The results show that the optimized loading path greatly improves the uniformity of wall thickness and the forming effect compared with the linear loading path.The round corner lamination rate of the tube is 91.2%under the fuzzy control optimized loading path,which was increased by 47.1%and 22.6%compared with linear loading Path 1 and Path 2,respectively.Based on the optimized loading path in the experiment,the minimum wall thickness of the variable diameter tube was 1.32 mm and the maximum thinning rate was 12.4%.The experimental results were consistent with the simulation results,which verified the accuracy of fuzzy control.The research results provide a reference for improving the forming quality of thin-walled tubes and plates.展开更多
In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new se...In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.展开更多
This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide...This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.展开更多
Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number...Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number of actuators,and there are problems with structural coupling and large temperature increases in their internal coils.Additionally,parameters of the traditional proportional integral derivative(PID)control cannot be adjusted in real-time to adapt to system changes.These problems can be addressed by introducing fuzzy control methods.A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process,and a prototype platform has been established to verify the effectiveness and robustness of this process.Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative(Fuzzy-PID)controllers,showing that,in system step response tests,the fuzzy control system reduces rise time by 20.25%,decreases overshoot by 78.24%,and shortens settling time by 67.59%.In disturbance rejection experiments,fuzzy control achieves a 46.09%reduction in the maximum deviation,indicating stronger robustness.The Fuzzy-PID controller,based on table lookup,outperforms the standard controller significantly,showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.展开更多
A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simul...A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simula-tion results show that the controller has not only simple nonlinear control of tfuzzy control, but also the learning capabil-ity and adaptability of neural netwrk.展开更多
This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. ...This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.展开更多
The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator fa...The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.展开更多
基金supported partially by the National Natural Science Foundation of China under Grant 61503348the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010the 111 project under Grant B17040
文摘When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.
基金National Natural Science Foundation of China and Provincial Natural Science Foundafion of Guangdong, China.
文摘An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.
基金The author extends their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFPSAU-2021/01/18128).
文摘The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.
基金supported by the National Natural Science Foundation of China (62073303,61673356)Hubei Provincial Natural Science Foundation of China (2015CFA010)the 111 Project(B17040)。
文摘This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
文摘The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.
文摘为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。
文摘This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.
文摘Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high ripple torque is obtained. In order to reduce this ripple, a control strategy with modified current shapes is proposed. A workbench consisting of a machine prototype and the control system based on a microcontroller was built. These controllers were: a conventional PID, a fuzzy logic PID and a neural PID type. From experimental results, the effective reduction of the torque ripple was confirmed and the performance of the controllers was compared.
文摘With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system has always been the focus of research. Automatic parking modules can greatly assist drivers in parking operations, greatly reduce parking difficulties and make people more convenient and fast parking. In this paper, an automatic parking system based on the fuzzy controller is proposed. The fuzzy controller of automatic parking system is constructed by using fuzzy theory, and the robustness of the whole system is examined by fuzzy logic. Firstly, the vehicle motion rules and trajectory changes are analyzed in detail, and the real parking lot model is simulated. Then, the input and output variables of the whole system are analyzed by fuzzy theory and the membership function is constructed. Based on the experience of human experts, the parking rules are tested and summarized, and a reasonable and practical rule base is established. Finally, MATLAB is used to code, build the visual interface of parking lot and vehicles, and draw the cyclic iterative function to detect the vehicle position and direction angle, so as to act as a sensor. The results show that using a fuzzy controller to construct an automatic parking system can effectively improve the parking level.
基金CONAHCYTTecnológico Nacional de Mexico/Tijuana Institute of Technology for the support during this research
文摘In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.
基金supported by the Shenyang Science and Technology Program(grant number 22-301-1-10).
文摘The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization.In this paper,the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object.Fuzzy control was used to optimize the loading path,and the fuzzy rule base was established based on FEM.The minimum wall thickness and wall thickness reduction rate were determined as input membership functions,and the axial feeds variable value of the next step was used as output membership functions.The results show that the optimized loading path greatly improves the uniformity of wall thickness and the forming effect compared with the linear loading path.The round corner lamination rate of the tube is 91.2%under the fuzzy control optimized loading path,which was increased by 47.1%and 22.6%compared with linear loading Path 1 and Path 2,respectively.Based on the optimized loading path in the experiment,the minimum wall thickness of the variable diameter tube was 1.32 mm and the maximum thinning rate was 12.4%.The experimental results were consistent with the simulation results,which verified the accuracy of fuzzy control.The research results provide a reference for improving the forming quality of thin-walled tubes and plates.
基金This research is financially supported by the Ministry of Science and Technology of China(Grant No.2019YFE0112400)the Department of Science and Technology of Shandong Province(Grant No.2021CXGC011204).
文摘In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.
基金supported by the National Natural Science Foundation of China(61973105,62373137)。
文摘This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.
基金supported by the National Key R&D Program of China (2022YFA1603001,2021YFC2801402)the National Nature Science Foundation of China (12073053)the Science and Technology Plan of Inner Mongolia (2021GG0245).
文摘Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number of actuators,and there are problems with structural coupling and large temperature increases in their internal coils.Additionally,parameters of the traditional proportional integral derivative(PID)control cannot be adjusted in real-time to adapt to system changes.These problems can be addressed by introducing fuzzy control methods.A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process,and a prototype platform has been established to verify the effectiveness and robustness of this process.Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative(Fuzzy-PID)controllers,showing that,in system step response tests,the fuzzy control system reduces rise time by 20.25%,decreases overshoot by 78.24%,and shortens settling time by 67.59%.In disturbance rejection experiments,fuzzy control achieves a 46.09%reduction in the maximum deviation,indicating stronger robustness.The Fuzzy-PID controller,based on table lookup,outperforms the standard controller significantly,showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.
文摘A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simula-tion results show that the controller has not only simple nonlinear control of tfuzzy control, but also the learning capabil-ity and adaptability of neural netwrk.
基金the National Natural Science Foundation of China under Grant U22A2043.
文摘This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.
基金partially supported by the National Natural Science Foundation of China(62322307)Sichuan Science and Technology Program,China(2023NSFSC1968).
文摘The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.
