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Development of fuzzy logic system to Predict the SAW Weldment Shape Profiles

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Journal of Marine Science and Application 2012年第3期11卷 387-391页

作者： H. K. Narang M. M. Mahapatra P K. Jha P. Biswas Department of Mechanical & Industrial Engineering llTRoorkee Roorkee 247667 India Department of Mechanical Engineering HT GuwahatL Guwahati 781039 India

A fuzzy model was presented to predict the weldment shape profile of submerged arc welds （SAW） including the shape of heat affected zone （HAZ）. The SAW bead-on-plates were welded by following a full factorial design matrix. The design matrix consisted of three levels of input welding process parameters. The welds were cross-sectioned and etched, and the zones were measured. A mapping technique was used to measure the various segments of the weld zones. These mapped zones were used to build a fuzzy logic model. The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone. The fuzzy model was further tested for a set of test case data. The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted. The mapping technique developed for the weld zones and the fuzzy logiemodel earl be used for on-line control of the SAW process. From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.

关键词： submerged arc welding (SAW) fuzzy-logic controller bead height weldment cross-sectional-area heat affected zone (HAZ) fuzzy model fuzzy logic system

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...

Stable Adaptive Nonlinear Control system Using fuzzy logic

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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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 *** schemes employ a fuzzy controller and a compensation *** parameters of the fuzzy controller are on line adjusted according to an adaptive law while the identification of the controlled plant is *** is proved that the closed loop system is asymptotically stable based on the Lyapunov synthesis ***,these schemes are applied to control a chaotic system. 

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

Bounded Control of Robotic Manipulators via fuzzy logic systems 36

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Bounded Control of Robotic Manipulators via Fuzzy Logic Syst...

fuzzy Adaptive State Estimation of Distributed Drive Electric Vehicles with Random Missing Measurements and Unknown Process Noise

第36届中国控制会议

作者： Linghuan Kong Wei He Yiting Dong Chenguang Yang Guang Li School of Automation Engineering and Center for Robotics University of Electronic Science and Technology of China School of Automation and Electrical Engineering University of Science and Technology Beijing Department of Mechanical Engineering Texas Tech University Zienkiewicz Centre for Computational Engineering Swansea University School of Engineering and Materials Science Queen Mary University of London

ISBN: (纸本)9781538629185

In this paper, a fuzzy neural network controller is proposed for a class of robotic manipulators with unknown dynamics and input constraints. First, to cope with the unknown dynamics, fuzzy logic systems are designed to approximate it. Second,considering the case where an aggressive control input may lead to the poor performance, even resulting in instability in practice,the controller is designed with the function tanh（·）, and the function has the ability to make the controller keep in the predefined range. At last, evidence based on the Lyapunov theory proves the system errors converge to a small range near zero. Simulations are carried out to show the effectiveness of the designed control.

关键词： fuzzy logic system Neural Networks Input Constraints Robotic Manipulators

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Chinese Journal of Mechanical Engineering 2024年第5期37卷 543-554页

作者： Zhiguo Zhang Guodong Yin Chao Huang Jingyu Hu Xing Xu Chengyue Jiang Yan Wang School of Mechanical Engineering Southeast UniversityNanjing 211189China Department of Industrial and system Engineering The Hong Kong Polytechnical UniversityHong KongChina Department of Automotive Engineering Research Institute Jiangsu UniversityZhenjiang 225009China College of Vehicle Engineering Chongqing University of TechnologyChongqing 400054China

Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric ***,as these states are not directly measured,Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate ***,existing methods tend to ignore the impact of data loss on estimation ***,the process noise,which changes dynamically due to varying driving conditions,is not adequately *** response to these constraints,we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter(FAFTEKF).Initially,a fault-tolerant EKF is devised to handle missing ***,a fuzzy logic system that dynamically updates the process noise matrix,is built to improve estimation accuracy under different driving *** experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.

关键词： Distributed drive electric vehicles State estimation Fault-tolerant EKF fuzzy logic system

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

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Chinese Journal of Chemical Engineering 2024年第12期76卷 251-263页

作者： Haokun Xiong Lei Xie Cheng Hu Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China

Proton exchange membrane(PEM)electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production *** accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis *** work developed a Koopman model predictive control(MPC)method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer.A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear *** identified linear predictors are used to design the Koopman *** addition,the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch *** effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

关键词： Hydrogen production PEM electrolyzer Nonlinear control Model predictive control Koopman operator fuzzy logic system

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 *** acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health *** 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 *** blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney ***,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 ***,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 *** the quick and accurate evaluation of the proposed system,Matlab version 2019 was *** proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%*** 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

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 *** 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 *** 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 *** results are also predicted by employing the tool of ANFIS 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 ***,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)