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Tsinghua Science and Technology 1997年第3期2卷 75-80页

作者：汤跃忠张乃尧李衍达

Two adaptive fuzzy control schemes for a class of nonlinear continuous dynamic systems are proposed.The schemes employ a fuzzy controller and a compensation controller.The parameters of the fuzzy controller are on line adjusted according to an adaptive law while the identification of the controlled plant is unnecessary.It is proved that the closed loop system is asymptotically stable based on the Lyapunov synthesis approach.Finally,these schemes are applied to control a chaotic system. 

关键词： nonlinear control control stability fuzzy logic system compensation control

A New Modeling Method for Switched Reluctance Motor Based on the fuzzy logic system

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A New Modeling Method for Switched Reluctance Motor Based on...

Second-Order Linear Multi-Agent Formation Control Based on fuzzy logic system Approximator and Actor-Critic Reinforcement Learning

第37届中国控制会议

作者： Jian Liu Guofeng Wang Cunhe Li Yan Li Yunsheng Fan Dongdong Mu College of Information Science and Technology Dalian Maritime University

This paper proposes a new modeling method for switched reluctance motor（SRM） based on fuzzy logic system with limited flux linkage samples. Firstly, the flux linkage characteristics at four torque balanced positions are obtained by using an indirect method excluding the rotor clamping devices. Secondly, based on the four flux-linkage curves, the fuzzy logic system is adopted to construct the entire flux linkage characteristics. According to the relationship of flux linkage with the rotor position, the space of the rotor position are divided into three regions. Then, the max products of degrees are calculated to generate the optimal fuzzy rules, and the rules are employed in the defuzzification. The main advantage is that the fuzzy rules are automatically extracted from limited samples without experience of experts. Finally, in order to verify the accuracy and feasibility of the proposed method, the flux linkage characteristics obtained by the proposed method are compared with those measured by rotor clamping method. The comparative results show that the proposed method has a good accuracy for SRM modeling.

关键词： Switched reluctance motor fuzzy logic system Torque balanced positions Flux linkage characteristics fuzzy rules

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Second-Order Linear Multi-Agent Formation Control Based on F...

fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

第三十九届中国控制会议

作者： Zipeng Zhang Jie Huang Fenghuang Cai Jian Chen Yutao Chen College of Electrical Engineering and Automation Fuzhou University

In this paper, an optimized leader-follower formation control for second-order linear multi-agent systems using the actor-critic reinforcement learning is proposed. An optimal control problem is formulated and solved using a combined actorcritic reinforcement learning and fuzzy logic systems（FLSs） approximator. Actor FLSs are constructed for performing control behavior and critic FLSs are responsible for evaluating the control effectiveness, respectively. Through theoretical analysis,we show that the desired optimal performance can be achieved. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method.

关键词： Actor-Critic reinforcement learning fuzzy logic system optimized formation control multi-agent systems

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Computer systems Science & Engineering 2023年第2期44卷 1583-1600页

作者： P.Rahul N.Kanthimathi B.Kaarthick M.Leeban Moses Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam638401India Department of Electronics and Communication Engineering Coimbatore Institute of Engineering and TechnologyCoimbatore641109India

Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is toﬁnd a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on fuzzy logic(FL)and increases the efﬁciency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourﬁdelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.

关键词： Ad-hoc load balancing H-MANET fuzzy logic system genetic algorithm node quality-based clustering algorithm improved weighted clustering fruitﬂy optimization

Optimum Design for the Magnification Mechanisms Employing fuzzy logic-ANFIS

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Computers, Materials & Continua 2022年第12期73卷 5961-5983页

作者： Ngoc Thai Huynh Tien V.T.Nguyen Quoc Manh Nguyen Faculty of Automobile Technology Industrial University of Ho Chi Minh CityHo Chi Minh City70000Vietnam Faculty of Mechanical Technology Industrial University of Ho Chi Minh CityHo Chi Minh City70000Vietnam Faculty of Mechanical Engineering Hung Yen University of Technology and EducationHung Yen160000Vietnam

To achieve high work performance for compliant mechanisms of motion scope,continuous work condition,and high frequency,we propose a new hybrid algorithm that could be applied to multi-objective optimum design.In this investigation,we use the tools of finite element analysis(FEA)for a magnificationmechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements.A poly-algorithm including the Grey-Taguchi method,fuzzy logic system,and adaptive neuro-fuzzy inference system(ANFIS)algorithm,was utilized mainly in this study.The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade.The results are also predicted by employing the tool of ANFIS in MATLAB.In conclusion,the optimal findings obtained:Its magnification is larger than 40 times in comparison with the initial design,the maximum principal stress is 127.89MPa,and the first modal shape frequency obtained 397.45 Hz.Moreover,we found that the outcomes obtained deviation error compared with predicted results of displacement,stress,and frequency are 8.76%,3.6%,and 6.92%,respectively.

关键词： Compliant mechanism grey relational analysis taguchi method multi-objective optimization fuzzy logic system adaptive neuro-fuzzy inference system(ANFIS)

Artificial intelligence:New age of transformation in petroleum upstream

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Petroleum Research 2022年第1期7卷 106-114页

作者： Parth Solanki Dhruv Baldaniya Dhruvikkumar Jogani Bhavesh Chaudhary Manan Shah Ameya Kshirsagar School of Petroleum Technology Pandit Deendayal Petroleum UniversityGandhinagar382007GujaratIndia Department of Chemical Engineering School of TechnologyPandit Deendayal Petroleum UniversityGandhinagar382007GujaratIndia Department of Information Technology Symbiosis Institute of TechnologyPuneMaharashtraIndia

In the Oil and Gas industry,the implementation of artificial intelligence techniques gives advantages of better use of existing infrastructure.It provides better future outcomes,which makes it an essential technology in the operations of the industry.In this paper,the implementation of artificial intelligence techniques for good planning,determination of reservoir rock properties,drilling optimization and production facilities are reviewed.With precise knowledge of permeability and porosity,reservoir engineers can develop robust reservoir development plan and manage hydrocarbon recovery effectively.For porosity prediction,wire-line log data and seismic attributes are used in artificial intelligence-based models.Permeability prediction has been discussed by considering the different types of well-log data in the modeling of artificial intelligence techniques to predict single-phase fluid.Solution of specific developed problems during the determination of permeability in the actual experiment is also mentioned.Artificial intelligence techniques are used in the design and accurate analysis of the drilling fluid.It assists in the selection of drill bit and drilling-related problems.If a reservoir cannot produce naturally,we can select a suitable artificial lift and design the production system using Artificial intelligence techniques.For EOR methods,SADA and NAVA knowledge base systems are discussed.Artificial intelligence can help in optimizing production,improve production efficiency and recovery of hydrocarbons.

关键词： Artificial neural network Enhanced oil recovery fuzzy logic system Artificial lift Permeability Porosity

Adaptive output consensus for heterogeneous nonlinear multi-agent systems with multi-type input constraints under switching-directed topologies

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Control Theory and Technology 2021年第2期19卷 260-272页

作者： Wei Zhang Zhaoxu Yu Shugang Li Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai200237China Department of Information Management Shanghai UniversityShanghai200444China

This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies,in which all followers are subjected to multi-type input constraints such as unknown asymmetric saturation,unknown dead-zone and their integration.A unified representation is presented to overcome the difficulties originating from multi-agent input constraints.Moreover,the uncertain system functions in a non-lower triangular form and the interaction terms among agents are dealt with by exploiting the fuzzy logic systems and their special property.Furthermore,by introducing a nonlinear filter to alleviate the problem of“explosion of complexity”during the backstepping design,a distributed common adaptive control protocol is proposed to ensure that the synchronization errors converge to a small neighborhood of the origin despite the existence of multiple input constraints and arbitrary switching communication topologies.Both stability analysis and simulation results are conducted to show the effectiveness and performance of the proposed control methodology.

关键词： Nonlinear multi-agent system Output consensus fuzzy logic system Input constraint Switching topology

Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

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Computers, Materials & Continua 2021年第5期67卷 1399-1412页

作者： Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally School of Computer Sciences National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science&IT Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Virtual University of PakistanIslamabad45000Pakistan Department of Information systems College of Computer and Information SciencesJouf UniversitySkakaAljouf72341Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversitySkakaAljouf72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt

Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.

关键词： fuzzy logic system artificial neural network deep extreme machine learning feed-backward propagation SMOIKD-FLS SMOIKD-ANN SMOIKD-DEML SMOIKD-FLS-ANN-DEML

Adaptive fuzzy Dynamic Surface Control for Uncertain Nonlinear systems

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International Journal of Automation and computing 2009年第4期6卷 385-390页

作者： Xiao-Yuan Luo Zhi-Hao Zhu Xin-Ping Guan Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 PRC

In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of ＂explosion of complexity＂ in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globally uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller （SMC）. Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.

关键词： Uncertain nonlinear systems fuzzy logic system dynamic surface control backstepping design