期刊文献+
共找到642篇文章
< 1 2 33 >
每页显示 20 50 100
Adaptive control of parallel manipulators via fuzzy-neural network algorithm 被引量:3
1
作者 Dachang ZHU Yuefa FANG 《控制理论与应用(英文版)》 EI 2007年第3期295-300,共6页
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA). With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric u... This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA). With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF. 展开更多
关键词 Parallel manipulator adaptive control fuzzy neural network algorithm SIMULATION
下载PDF
Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks 被引量:2
2
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.
下载PDF
Adaptive fuzzy synchronization for a class of fractional-order neural networks 被引量:1
3
作者 刘恒 李生刚 +1 位作者 王宏兴 李冠军 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第3期258-267,共10页
In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as sync... In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors, are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters, fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method. 展开更多
关键词 fractional-order neural network adaptive fuzzy control fractional-order adaptation law
下载PDF
Development of an electrode intelligent design system based on adaptive fuzzy neural network and genetic algorithm
4
作者 Huang Jun Xu Yuelan +1 位作者 Wang Luyuan Wang Kehong 《China Welding》 EI CAS 2014年第2期62-66,共5页
The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical propertie... The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical properties of deposited metals directly according to the components of coating on the electrodes. In this paper an electrode intelligent design system is developed by means of fuzzy neural network technology and genetic algorithm,, dynamic link library, object linking and embedding and multithreading. The front-end application and customer interface of the system is realized by using visual C ++ program language and taking SQL Server 2000 as background database. It realizes series functions including automatic design of electrode formula, intelligent prediction of electrode properties, inquiry of electrode information, output of process report based on normalized template and electronic storage and search of relative files. 展开更多
关键词 electrode design system adaptive fuzzy neural network genetic algorithm object linking and embedding
下载PDF
Research on Financial Distress Prediction with Adaptive Genetic Fuzzy Neural Networks on Listed Corporations of China
5
作者 Zhibin XIONG 《International Journal of Communications, Network and System Sciences》 2009年第5期385-391,共7页
To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model... To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models. 展开更多
关键词 MULTI-POPULATION adaptive GENETIC BP Algorithm fuzzy neural network Cross Validation FINANCIAL DISTRESS
下载PDF
Comparison between Neural Network and Adaptive Neuro-Fuzzy Inference System for Forecasting Chaotic Traffic Volumes
6
作者 Jiin-Po Yeh Yu-Chen Chang 《Journal of Intelligent Learning Systems and Applications》 2012年第4期247-254,共8页
This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the ... This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance. 展开更多
关键词 neural network adaptive NEURO-fuzzy INFERENCE System CHAOTIC TRAFFIC VOLUMES State Space Reconstruction
下载PDF
Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria
7
作者 Djeldjli Halima Benatiallah Djelloul +3 位作者 Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra 《Computers, Materials & Continua》 SCIE EI 2024年第6期4725-4740,共16页
When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global s... When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes. 展开更多
关键词 Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation
下载PDF
A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems 被引量:2
8
作者 Li Shaoyuan & Xi Yugeng (Shanghai Jiaotong University, 200030, P. R. China) 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2000年第1期61-66,共6页
In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neu... In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications. 展开更多
关键词 fuzzy logic neural networks adaptive control Nonlinear dynamic system.
下载PDF
HPSO-based fuzzy neural network control for AUV 被引量:1
9
作者 Lei ZHANG Yongjie PANG Yumin SU Yannan LIANG 《控制理论与应用(英文版)》 EI 2008年第3期322-326,共5页
A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimiz... A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization (HPSO) algorithm based on immune theory and nonlinear decreasing inertia weight (NDIW) strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs. 展开更多
关键词 Autonomous underwater vehicle fuzzy neural network Model reference adaptive control Particle swarm optimization algorithm Immune theory
下载PDF
Modelling and control PEMFC using fuzzy neural networks 被引量:1
10
作者 孙涛 闫思佳 +1 位作者 曹广益 朱新坚 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第10期1084-1089,共6页
Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system in... Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermo-dynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system. 展开更多
关键词 Proton exchange membrane fuel cell adaptive neural-networks fuzzy infer system MODELING neural network
下载PDF
Characteristics Prediction Method of Electro-hydraulic Servo Valve Based on Rough Set and Adaptive Neuro-fuzzy Inference System 被引量:11
11
作者 JIA Zhenyuan MA Jianwei WANG Fuji LIU Wei 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2010年第2期200-208,共9页
Synthesis characteristics of the electro-hydraulic servo valve are key factors to determine eligibility of the hydraulic production. Testing all synthesis characteristics of the electro-hydraulic servo valve after ass... Synthesis characteristics of the electro-hydraulic servo valve are key factors to determine eligibility of the hydraulic production. Testing all synthesis characteristics of the electro-hydraulic servo valve after assembling leads to high repair rate and reject rate, so accurate prediction for the synthesis characteristics in the industrial production is particular important in decreasing the repair rate and the reject rate of the product. However, the research in forecasting synthesis characteristics of the electro-hydraulic servo valve is rare. In this work, a hybrid prediction method was proposed based on rough set(RS) and adaptive neuro-fuzzy inference system(ANFIS) in order to predict synthesis characteristics of electro-hydraulic servo valve. Since the geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve are from workers' experience, the inputs of the prediction method are uncertain. RS-based attributes reduction was used as the preprocessor, and then the exact geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve were obtained. On the basis of the exact geometric factors, ANFIS was used to build the final prediction model. A typical electro-hydraulic servo valve production was used to demonstrate the proposed prediction method. The prediction results showed that the proposed prediction method was more applicable than the artificial neural networks(ANN) in predicting the synthesis characteristics of electro-hydraulic servo valve, and the proposed prediction method was a powerful tool to predict synthesis characteristics of the electro-hydraulic servo valve. Moreover, with the use of the advantages of RS and ANFIS, the highly effective forecasting framework in this study can also be applied to other problems involving synthesis characteristics forecasting. 展开更多
关键词 characteristics prediction rough set adaptive neuro-fuzzy inference system electro-hydraulic servo valve artificial neural networks
下载PDF
Fault-Tolerant Control of Nonlinear Systems Based on Fuzzy Neural Networks 被引量:1
12
作者 左东升 姜建国 《Journal of Donghua University(English Edition)》 EI CAS 2009年第6期634-638,共5页
Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tole... Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters' variation fault and load torque disturbance. 展开更多
关键词 fuzzy neural networks nonlinear sYStem fault-tolerant control adaptive
下载PDF
Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis 被引量:3
13
作者 LIU Bo WANG Yong WANG Hong-jian 《哈尔滨工程大学学报》 EI CAS CSCD 北大核心 2006年第B07期547-551,共5页
关键词 聚类分析 遗传算法 模糊自适应谐振理论 人工神经网络
下载PDF
Fuzzy Neural Network PID Controller for seam tracking 被引量:2
14
作者 XiaoMin ZhangHua 《微计算机信息》 北大核心 2006年第09S期71-73,共3页
A Fuzzy Neural Network PID Controller is proposed in this paper, Fuzzy Neural Network Controller is used to optimize the parameters of PID Controller real time. Computer simulation using MATLAB shows that, comparing t... A Fuzzy Neural Network PID Controller is proposed in this paper, Fuzzy Neural Network Controller is used to optimize the parameters of PID Controller real time. Computer simulation using MATLAB shows that, comparing to the classical PID Controller, Fuzzy Neural Network PID Controller can improve the precision of control results for seam tracking. 展开更多
关键词 自适应PID 模糊神经网络 焊缝跟踪 模糊控制器
下载PDF
Employing adaptive fuzzy computing for RCP intelligent control and fault diagnosis
15
作者 Ashraf Aboshosha Hisham A.Hamad 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第9期82-93,共12页
Loss of coolant accident(LOCA),loss of fluid accident(LOFA),and loss of vacuum accident(LOVA)are the most severe accidents that can occur in nuclear power reactors(NPRs).These accidents occur when the reactor loses it... Loss of coolant accident(LOCA),loss of fluid accident(LOFA),and loss of vacuum accident(LOVA)are the most severe accidents that can occur in nuclear power reactors(NPRs).These accidents occur when the reactor loses its cooling media,leading to uncontrolled chain reactions akin to a nuclear bomb.This article is focused on exploring methods to prevent such accidents and ensure that the reactor cooling system remains fully controlled.The reactor coolant pump(RCP)has a pivotal role in facilitating heat exchange between the primary cycle,which is connected to the reactor core,and the secondary cycle associated with the steam generator.Furthermore,the RCP is integral to preventing catastrophic events such as LOCA,LOFA,and LOVA accidents.In this study,we discuss the most critical aspects related to the RCP,specifically focusing on RCP control and RCP fault diagnosis.The AI-based adaptive fuzzy method is used to regulate the RCP’s speed and torque,whereas the neural fault diagnosis system(NFDS)is implemented for alarm signaling and fault diagnosis in nuclear reactors.To address the limitations of linguistic and statistical intelligence approaches,an integration of the statistical approach with fuzzy logic has been proposed.This integrated system leverages the strengths of both methods.Adaptive fuzzy control was applied to the VVER 1200 NPR-RCP induction motor,and the NFDS was implemented on the Kori-2 NPR-RCP. 展开更多
关键词 Nuclear power plant(NPP) Reactor coolant pump Fault diagnosis Reactor passive safety neural network adaptive fuzzy
下载PDF
Adaptive fuzzy design of a rope attitude adjustment system
16
作者 WANG Wei-shu MENG Li-fan ZHANG Zhi-dong 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第2期168-175,共8页
The attitude adjustment of rope system faces the challenging due to the difficulty in obtaining accurate three-dimensional(3D)mathematical model and solving by traditional methods.A set of adjustment systems is design... The attitude adjustment of rope system faces the challenging due to the difficulty in obtaining accurate three-dimensional(3D)mathematical model and solving by traditional methods.A set of adjustment systems is designed and used to investigate the automatic control for level or preset attitude adjustment of unknown weights and eccentric loads.The system principle and characteristics are analyzed.The 3D model is decomposed into two two-dimensional(2D)subsystems,and an adaptive fuzzy controller based on BP neural network and least squares(LSE)is designed.The simulation experiment uses MATLAB to train the level-adjustment data for testing algorithm,and a small load is used to verify the effectiveness of the system.The experimental results show that precise attitude adjustment can be achieved within the system load range,and the response speed is fast.This adjustment method provides a fast and effective method for precise adjustment of the load attitude. 展开更多
关键词 attitude adjustment rope system modelling adaptive fuzzy controller BP neural network
下载PDF
A New Neuro-Fuzzy Adaptive Genetic Algorithm
17
作者 ZHU Lili ZHANG Huanchun JING Yazhi(Faculty 302,Nanjing University of Aeronautics and Astronautics,Nanjing 210016 China) 《Journal of Electronic Science and Technology of China》 2003年第1期63-68,共6页
Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to contro... Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization. 展开更多
关键词 genetic algorithm fuzzy logic control CMAC neural network adaptive parameter control
下载PDF
Composite Adaptive Control of Belt Polishing Force for Aero-engine Blade 被引量:12
18
作者 ZHsAO Pengbing SHI Yaoyao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2013年第5期988-996,共9页
The existing methods for blade polishing mainly focus on robot polishing and manual grinding.Due to the difficulty in high-precision control of the polishing force,the blade surface precision is very low in robot poli... The existing methods for blade polishing mainly focus on robot polishing and manual grinding.Due to the difficulty in high-precision control of the polishing force,the blade surface precision is very low in robot polishing,in particular,quality of the inlet and exhaust edges can not satisfy the processing requirements.Manual grinding has low efficiency,high labor intensity and unstable processing quality,moreover,the polished surface is vulnerable to burn,and the surface precision and integrity are difficult to ensure.In order to further improve the profile accuracy and surface quality,a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed,which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together.By the mode decision-making mechanism,Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value,and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision.Based on the mathematical model of the force-exerting mechanism,simulation analysis is implemented on DSCAC.Simulation results show that the output polishing force can better track the given signal.Finally,the blade polishing experiments are carried out on the designed polishing equipment.Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility,valve dead-time effect,valve nonlinear flow,cylinder friction,measurement noise and other interference on the control precision of polishing force,which has high control precision,strong robustness,strong anti-interference ability and other advantages compared with MRACFNN.The proposed research achieves high-precision control of the polishing force,effectively improves the blade machining precision and surface consistency,and significantly reduces the surface roughness. 展开更多
关键词 BLADE polishing force Bang-Bang control fuzzy neural network model reference adaptive control
下载PDF
Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control 被引量:11
19
作者 张友旺 桂卫华 《Journal of Central South University of Technology》 EI 2008年第2期256-263,共8页
Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe... Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively. 展开更多
关键词 electro-hydraulic servo system adaptive dynamic recurrent fuzzy neural network(ADRFNN) gain adaptive slidingmode variable structure control(GASMVSC) secondary uncertainty
下载PDF
Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach 被引量:4
20
作者 ZHANG Song-tao REN Guang 《Journal of Marine Science and Application》 2006年第3期5-10,共6页
This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an ... This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results. 展开更多
关键词 fuzzy neural network ship course-tracking adaptive control backstepping approach
下载PDF
上一页 1 2 33 下一页 到第
使用帮助 返回顶部