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Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC
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作者 沈承 Cao +2 位作者 Guangyi Zhu Xinjian 《High Technology Letters》 EI CAS 2002年第2期76-82,共7页
Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial... Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations. 展开更多
关键词 Molten Carbonate Fuel Cells (MCFC) Radial Basis Function (RBF) fuzzy neural networks control modelling
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Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks 被引量:2
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作者 Shao-Cheng Tong Yong-Ming Li 《International Journal of Automation and computing》 EI 2009年第2期145-153,共9页
In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the ... In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach. 展开更多
关键词 Nonlinear systems backstepping control adaptive fuzzy neural networks control state observer output feedback control.
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Adaptive neural networks control for uncertain parabolic distributed parameter systems with nonlinear periodic time-varying parameter 被引量:1
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作者 LEI YanFang LI JunMin ZHAO AiLiang 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2022年第7期1482-1492,共11页
This paper studies the problem of adaptive neural networks control(ANNC) for uncertain parabolic distributed parameter systems(DPSs) with nonlinear periodic time-varying parameter(NPTVP). Firstly, the uncertain nonlin... This paper studies the problem of adaptive neural networks control(ANNC) for uncertain parabolic distributed parameter systems(DPSs) with nonlinear periodic time-varying parameter(NPTVP). Firstly, the uncertain nonlinear dynamic and unknown periodic TVP are represented by using neural networks(NNs) and Fourier series expansion(FSE), respectively. Secondly, based on the ANNC and reparameterization approaches, two control algorithms are designed to make the uncertain parabolic DPSs with NPTVP asymptotically stable. The sufficient conditions of the asymptotically stable for the resulting closed-loop systems are also derived. Finally, a simulation is carried out to verify the effectiveness of the two control algorithms designed in this work. 展开更多
关键词 nonlinear parabolic distributed parameter systems adaptive neural networks control Fourier series expansion asymptotically stable
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The adaptive control using BP neural networks for a nonlinear servo-motor 被引量:2
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作者 Xinliang ZHANG Yonghong TAN 《控制理论与应用(英文版)》 EI 2008年第3期273-276,共4页
The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathema... The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification (NNI) and control (NNC), respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness. 展开更多
关键词 Servo-motor NONLINEARITY neural networks based control Lyapunov stability Learning rate
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Adaptive Control of Flexible Redundant Manipulators Using Neural Networks 被引量:2
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作者 宋轶民 李建新 +1 位作者 王世宇 刘建平 《Transactions of Tianjin University》 EI CAS 2006年第6期429-433,共5页
An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted. The smart links of the manipulator were synthesized with the flexible links to which were attach... An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted. The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors. A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator. The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced. The neuro-identifier and the neuro-controller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC. By adjusting the neuro-identifier and the neuro-controller alternatively, the manipulator was controlled on line for achieving the desired dynamic performance. Finally, a planar 3R redundant manipulator with one smart link was utilized as an illustrative example. The simulation results proved the validity of the control strategy. 展开更多
关键词 flexible manipulators kinematic redundancy active vibration control neural networks adaptive control
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Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control 被引量:1
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作者 王炜 曾红兵 Kok-Lay Teo 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第11期127-134,共8页
This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopte... This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality(LMI) technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods. 展开更多
关键词 neural networks synchronization sampled-data control free-matrix-based inequality
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Synthetical Control of AGC/LPC System Based on Neural Networks Internal Model Control
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作者 Hu He, Xiaodong Luan, Yikang Sun Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China 《Journal of University of Science and Technology Beijing》 CSCD 2001年第1期75-77,共3页
One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neu... One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective. 展开更多
关键词 hot strip rolling AGC LOOPER neural networks internal model control GA
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FINITE-TIME H∞ CONTROL FOR A CLASS OF MARKOVIAN JUMPING NEURAL NETWORKS WITH DISTRIBUTED TIME VARYING DELAYS-LMI APPROACH
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作者 p.baskar s.padmanabhan m.syed ali 《Acta Mathematica Scientia》 SCIE CSCD 2018年第2期561-579,共19页
In this article, we investigates finite-time H∞ control problem of Markovian jumping neural networks of neutral type with distributed time varying delays. The mathematical model of the Markovian jumping neural networ... In this article, we investigates finite-time H∞ control problem of Markovian jumping neural networks of neutral type with distributed time varying delays. The mathematical model of the Markovian jumping neural networks with distributed delays is established in which a set of neural networks are used as individual subsystems. Finite time stability analysis for such neural networks is addressed based on the linear matrix inequality approach. Numerical examples are given to illustrate the usefulness of our proposed method. The results obtained are compared with the results in the literature to show the conservativeness. 展开更多
关键词 Finite-time H∞ control Markovian jumping neural networks Lyapunov stability
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Control of Halo-Chaos in Beam Transport Network via Neural Network Adaptation with Time-Delayed Feedback 被引量:4
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作者 FANG Jin-Qing LUO Xiao-Shu Guo-Xian 《Communications in Theoretical Physics》 SCIE CAS CSCD 2006年第1期117-120,共4页
Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field ad... Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network. 展开更多
关键词 beam transport network periodic focusing channels high-current proton beam HALO-CHAOS neural network adaptation control time-delayed feedback
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COMPUTER SIMULATION OF NEURAL NETWORK CONTROL SYSTEM FOR CO_2 WELDING PROCESS 被引量:3
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作者 D. Fan B. Li Y.Z. Ma and J.H. Chen (Welding Institute, Gansu University of Technology,Lanzhou 730050, China) 《Acta Metallurgica Sinica(English Letters)》 SCIE EI CAS CSCD 2000年第1期187-193,共7页
In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I &a... In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed. 展开更多
关键词 welding spatter neural network control SIMULATION
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Application of PID Controller Based on BP Neural Network in Export Steam’s Temperature Control System 被引量:4
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作者 朱增辉 孙慧影 《Journal of Measurement Science and Instrumentation》 CAS 2011年第1期84-87,共4页
By combining the Back-Propagation (BP) neural network with conventional proportional Integral Derivative (PID) controller, a new temperature control strategy of the export steam in supercritical electric power pla... By combining the Back-Propagation (BP) neural network with conventional proportional Integral Derivative (PID) controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler's export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system. 展开更多
关键词 PID controller based on BP neural network supercritical power unit export steam temperature large timedelay
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CONTROL SCHEMES FOR CMAC NEURAL NETWORK-BASED VISUAL SERVOING 被引量:1
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作者 Wang HuamingXi WenmingZhu JianyingDepartment of Mechanical andElectrical Engineering,Nanjing University of Aeronauticsand Astronautics,Nanjing 210016, China 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2003年第3期256-259,共4页
In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of im... In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2. 展开更多
关键词 CMAC neural network control scheme Visual servoing
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Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems
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作者 Frank L.LEWIS 《控制理论与应用(英文版)》 EI 2007年第1期1-11,共11页
In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation o... In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem. 展开更多
关键词 Constrained input system Hamilton-Jacobi-Isaacs H-infinity control Finite-horizon zero-sum games neural network control
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Intelligent Controller for UPQC Using Combined Neural Network 被引量:3
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作者 Ragavan Saravanan Subramanian Manoharan 《Circuits and Systems》 2016年第6期680-691,共12页
The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can co... The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally. 展开更多
关键词 Unified Power Quality Conditioner (UPQC) Combined neural Network (CNN) controller Fuzzy Logic controller (FLC) Total Harmonic Distortion (THD)
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Adaptive Server Load Balancing in SDN Using PID Neural Network Controller
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作者 R.Malavika M.L.Valarmathi 《Computer Systems Science & Engineering》 SCIE EI 2022年第7期229-243,共15页
Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though ... Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots. 展开更多
关键词 Software defined networks PID neural network controller closed loop control theory server load balancing server response time
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A Direct Feedback Control Based on Fuzzy Recurrent Neural Network
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作者 李明 马小平 《Journal of China University of Mining and Technology》 2002年第2期215-218,共4页
A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algor... A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simulation results indicate that fuzzy recurrent neural network controller has perfect dynamic and static performances . 展开更多
关键词 fuzzy neural network genetic algorithm neural network control
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Comparative Analysis between Conventional PI, Fuzzy Logic and Artificial Neural Network Based Speed Controllers of Induction Motor with Considering Core Loss and Stray Load Loss
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作者 Md. Rifat Hazari Effat Jahan +1 位作者 Mohammad Abdul Mannan Junji Tamura 《Journal of Mechanics Engineering and Automation》 2017年第1期50-57,共8页
Most of the controllers of IM (induction motor) for industrial applications have been designed based on PI controller without consideration of CL (core loss) and SLL (stray load loss). To get the precise perform... Most of the controllers of IM (induction motor) for industrial applications have been designed based on PI controller without consideration of CL (core loss) and SLL (stray load loss). To get the precise performances of torque as well as rotor speed and flux, the above mentioned losses should be considered. Conventional PI controller has overshoot effect at the transient period of the speed response curve. On the other hand, fuzzy logic and ANN (artificial neural network) based controllers can minimize the overshoot effect at the transient period because they have the abilities to deal with the nonlinear systems. In this paper, a comparative analysis is done between PI, fuzzy logic and ANN based speed controllers to find the suitable control strategy for IM with consideration of CL and SLL. The simulation analysis is done by using Matlab/Simulink software. The simulation results show that the fuzzy logic based speed controller gives better responses than ANN and conventional PI based speed controllers in terms of rotor speed, electromagnetic torque and rotor flux of IM. 展开更多
关键词 Core loss stray load loss PI controller fuzzy logic controller artificial neural network controller
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Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller
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作者 S. Saravanan K. Geetha 《Circuits and Systems》 2016年第11期3670-3684,共15页
In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of ... In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software. 展开更多
关键词 Dynamic Model Low Torque Ripples neural Model neural Network Predictive controller Unstable Operation Single Phase Induction Motor Variable Speed Drives
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Advanced Control of a PMSG Wind Turbine
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作者 Hafsi Slah Dhaoui Mehdi Sbita Lassaad 《International Journal of Modern Nonlinear Theory and Application》 2016年第1期1-10,共10页
In this work, an intelligent artificial control of a variable speed wind turbine (PMSG) is proposed. First, a mathematical model of turbine written at variable speed is established to investigate simulations results. ... In this work, an intelligent artificial control of a variable speed wind turbine (PMSG) is proposed. First, a mathematical model of turbine written at variable speed is established to investigate simulations results. In order to optimize energy production from wind, a pitch angle and DC bus control law is synthesized using PI controllers. Then, an intelligent artificial control such as fuzzy logic and artificial neural network control is applied. Its simulated performances are then compared to those of a classical PI controller. Results obtained in MATLAB/Simulink environment show that the fuzzy and the neuro control is more robust and has superior dynamic performance and hence is found to be a suitable replacement of the conventional PI controller for the high performance drive applications. 展开更多
关键词 Wind Turbine Variable Speed Pitch Angle Pi controller Fuzzy Logic controller neural networks controller
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Intelligent Autonomous-Robot Control for Medical Applications 被引量:3
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作者 Rihem Farkh Haykel Marouani +3 位作者 Khaled Al Jaloud Saad Alhuwaimel Mohammad Tabrez Quasim Yasser Fouad 《Computers, Materials & Continua》 SCIE EI 2021年第8期2189-2203,共15页
The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patien... The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected patients.The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic robots.Health officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense medications to individual patients.An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction.These criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control. 展开更多
关键词 Autonomous medic robots PID control neural network control system real-time implementation navigation environment differential drive system
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