Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of ...Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.展开更多
This paper presents an analysis of the KM （Karnik-Mendel） algorithms performance under real time implementation using 3 types： the non-iterative, the iterative and the enhanced, and their feasibility for real-time ...This paper presents an analysis of the KM （Karnik-Mendel） algorithms performance under real time implementation using 3 types： the non-iterative, the iterative and the enhanced, and their feasibility for real-time interval type 2 fuzzy logic control system applications. The results are also compared against NT （Nie-Tan） method that is one of the fastest and simplest defuzzification methods. Because the DC （direct current） servo-motor is one of the most used motors in different industrial applications and the model of the motor is nonlinear, this motor was selected for validating the implementation in real time hardware. This DC motor is a perfect option for studying the real time performance of KM algorithms in order to show up its limits and possibilities for real-time control system applications. These methodologies are implemented in National Instruments LabVIEW FPGA （field programmable gate array） module hardware which is one of the most used platforms in the industry. The results show that the E-KM （enhanced KM） algorithm and the NT method present good results for implementing real-time control applications in real time hardware. Although fuzzy logic type 2 is a good option for working with nonlinear and noise from the sensors, the defuzzification method has to react in a short period of time in order to allow good control response. Hence, a complete study of defuzzification is needed for improving the real time implementations of fuzzy type 2.展开更多
This paper proposes an efficient method for designing accurate structure-specified mixed H2/H∞ optimal controllers for systems with uncertainties and disturbance using particle swarm (PSO) algorithm. It is designed t...This paper proposes an efficient method for designing accurate structure-specified mixed H2/H∞ optimal controllers for systems with uncertainties and disturbance using particle swarm (PSO) algorithm. It is designed to find a suitable controller that minimizes the performance index of error signal subject to an unequal constraint on the norm of the closed-loop system. Although the mixed H2/H∞ for the output feedback approach control is considered as a robust and optimal control technique, the design process normally comes up with a complex and non-convex optimization problem, which is difficult to solve by the conventional optimization methods. The PSO can efficiently solve design problems of multi-input-multi-output (MIMO) optimal control systems, which is very suitable for practical engineering designs. It is used to search for parameters of a structure-specified controller, which satisfies mixed performance index. The simulation and experimental results show high feasibility, robustness and practical value compared with the conventional proportional-integral-derivative (PID) and proportional-Integral (PI) controller, and the proposed algorithm is also more efficient compared with the genetic algorithm (GA).展开更多
Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of O...Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of Opportunity” and autocorrelation. In this paper, advanced T2 statistics model and neural networks scheme are combined to solve the above problems: use T2 statistics technique to solve the problem of autocorrelation;adopt neural networks technique to solve the problem of “Window of Opportunity” and identification of disturbance causes. At the same time, regarding the shortcoming of neural network technique that its algorithm has a low speed of convergence and it is usually plunged into local optimum easily. Genetic algorithm was proposed to train samples in this paper. Results of the simulation ex-periments show that this method can detect the process disturbance quickly and accurately as well as identify the dis-turbance type.展开更多
With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a ...With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.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
In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec...In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.展开更多
复杂非线性系统存在强非线性和不确定性等问题,其建模与控制一直是个极具挑战的工作。自适应逆控制是一种有效的非线性系统控制方法,已经得到广泛的研究;2型模糊系统采用2型模糊集,相比于1型模糊系统,其能够提供更大的自由度,不确定性...复杂非线性系统存在强非线性和不确定性等问题,其建模与控制一直是个极具挑战的工作。自适应逆控制是一种有效的非线性系统控制方法,已经得到广泛的研究;2型模糊系统采用2型模糊集,相比于1型模糊系统,其能够提供更大的自由度,不确定性及非线性处理能力更强,能够采用较少的规则数取得较高的建模与控制精度。因此,本文将2型模糊系统理论与自适应逆控制相结合,提出了一种基于区间2型T-S模糊系统的自适应逆控制方法,实现对复杂非线性系统的有效建模与控制。首先通过离线输出输入数据映射得到非线性系统的离线2型模糊逆模型,然后将该离线区间2型模糊逆模型作为初始控制器,与被控对象串联,进行在线控制,并采用最小均方差(Least Mean Square,LMS)滤波算法在线修正2型模糊逆模型的结论参数,通过数字复制,更新逆模型控制器的参数。最后将该方法应用于两个仿真实例,结果表明本文方法控制精度高,不确定性处理能力强。展开更多
文摘Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.
文摘This paper presents an analysis of the KM （Karnik-Mendel） algorithms performance under real time implementation using 3 types： the non-iterative, the iterative and the enhanced, and their feasibility for real-time interval type 2 fuzzy logic control system applications. The results are also compared against NT （Nie-Tan） method that is one of the fastest and simplest defuzzification methods. Because the DC （direct current） servo-motor is one of the most used motors in different industrial applications and the model of the motor is nonlinear, this motor was selected for validating the implementation in real time hardware. This DC motor is a perfect option for studying the real time performance of KM algorithms in order to show up its limits and possibilities for real-time control system applications. These methodologies are implemented in National Instruments LabVIEW FPGA （field programmable gate array） module hardware which is one of the most used platforms in the industry. The results show that the E-KM （enhanced KM） algorithm and the NT method present good results for implementing real-time control applications in real time hardware. Although fuzzy logic type 2 is a good option for working with nonlinear and noise from the sensors, the defuzzification method has to react in a short period of time in order to allow good control response. Hence, a complete study of defuzzification is needed for improving the real time implementations of fuzzy type 2.
文摘This paper proposes an efficient method for designing accurate structure-specified mixed H2/H∞ optimal controllers for systems with uncertainties and disturbance using particle swarm (PSO) algorithm. It is designed to find a suitable controller that minimizes the performance index of error signal subject to an unequal constraint on the norm of the closed-loop system. Although the mixed H2/H∞ for the output feedback approach control is considered as a robust and optimal control technique, the design process normally comes up with a complex and non-convex optimization problem, which is difficult to solve by the conventional optimization methods. The PSO can efficiently solve design problems of multi-input-multi-output (MIMO) optimal control systems, which is very suitable for practical engineering designs. It is used to search for parameters of a structure-specified controller, which satisfies mixed performance index. The simulation and experimental results show high feasibility, robustness and practical value compared with the conventional proportional-integral-derivative (PID) and proportional-Integral (PI) controller, and the proposed algorithm is also more efficient compared with the genetic algorithm (GA).
文摘Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of Opportunity” and autocorrelation. In this paper, advanced T2 statistics model and neural networks scheme are combined to solve the above problems: use T2 statistics technique to solve the problem of autocorrelation;adopt neural networks technique to solve the problem of “Window of Opportunity” and identification of disturbance causes. At the same time, regarding the shortcoming of neural network technique that its algorithm has a low speed of convergence and it is usually plunged into local optimum easily. Genetic algorithm was proposed to train samples in this paper. Results of the simulation ex-periments show that this method can detect the process disturbance quickly and accurately as well as identify the dis-turbance type.
基金supported in part by the National Natural Science Foundation of China under Grant 62071283,Grant 61771296,Grant 61872228 and Grant 62271513in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JQ6048 and Grant 2018JZ6006+3 种基金in part by Shaanxi Key Industrial Innovation Chain Project in Industrial Domain under Grant 2020ZDLGY15-09in part by Guang Dong Basic and Applied Basic Research Foundation under Grant 2021A1515012631in part by China Postdoctoral Science Foundation under Grant 2016M600761in part by the Fundamental Research Funds for the Central Universities under Grant GK202003075 and Grant GK202103016。
文摘With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.
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.
文摘In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.
文摘复杂非线性系统存在强非线性和不确定性等问题,其建模与控制一直是个极具挑战的工作。自适应逆控制是一种有效的非线性系统控制方法,已经得到广泛的研究;2型模糊系统采用2型模糊集,相比于1型模糊系统,其能够提供更大的自由度,不确定性及非线性处理能力更强,能够采用较少的规则数取得较高的建模与控制精度。因此,本文将2型模糊系统理论与自适应逆控制相结合,提出了一种基于区间2型T-S模糊系统的自适应逆控制方法,实现对复杂非线性系统的有效建模与控制。首先通过离线输出输入数据映射得到非线性系统的离线2型模糊逆模型,然后将该离线区间2型模糊逆模型作为初始控制器,与被控对象串联,进行在线控制,并采用最小均方差(Least Mean Square,LMS)滤波算法在线修正2型模糊逆模型的结论参数,通过数字复制,更新逆模型控制器的参数。最后将该方法应用于两个仿真实例,结果表明本文方法控制精度高,不确定性处理能力强。
