期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到840篇文章
< 1 2 … 42 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Neural Network Adaptive Hierarchical Sliding Mode Control for the Trajectory Tracking of a Tendon-Driven Manipulator 认领 引用 被引量：1
1
作者 Yudong Zhang Leiying He +2 位作者 Jianneng Chen Bo Yan Chuanyu Wu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第2期295-314,共20页
Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory ... Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory tracking control of the manipulator.This paper proposes the radial basis function neural network adaptive hierarchical sliding mode control(RBFNNA-HSMC)method,which combines the dynamic model of the elastic tendon-driven manipulator(ETDM)with radial basis neural network adaptive control and hierarchical sliding mode control technology.The aim is to achieve trajectory tracking control of ETDM even under conditions of model inaccuracy and disturbance.The Lyapunov stability theory demonstrates the stability of the proposed RBFNNA-HSM controller.In order to assess the effectiveness and adaptability of the proposed control method,simulations and experiments were performed on a two-DOF ETDM.The RBFNNA-HSM method shows superior tracking accuracy compared to traditional modelbased HSM control.The experiment shows that the maximum tracking error for ETDM double-joint trajectory tracking is below 2.593×10-3rad and 1.624×10-3rad,respectively. 展开更多
关键词 Elastic tendon-driven manipulator Flexible joint Hierarchical sliding mode control Neural network adaptive control Tracking control
暂未订购 下载PDF
An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes 认领 引用 被引量：8
2
作者 Viet-Anh Le Hai-Xuan Le +1 位作者 Linh Nguyen Minh-Xuan Phan 《International Journal of Automation and computing》 CSCD 2019年第5期614-627,共14页
In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfa... In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and reallife systems, where the results obtained by our method are highly promising. 展开更多
关键词 3D overhead crane adaptive control hierarchical sliding mode control neural network radial basis function
暂未订购 下载PDF
Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle 认领 引用 被引量：14
3
作者 Chao Yang Feng Yao Ming-Jun Zhang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2018年第6期228-243,共16页
The trajectory tracking control problem is addressed for autonomous underwater vehicle(AUV) in marine environ?ment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic ... The trajectory tracking control problem is addressed for autonomous underwater vehicle(AUV) in marine environ?ment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks(RNN) is proposed. Firstly, considering the inaccu?rate of thrust model of thruster, a Taylor’s polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty(SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering?reduction method is proposed based on sigmoid function. In chattering?reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapu?nov theory and Barbalat’s lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can e ectively achieve high?precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and e ectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool?experi?ments of AUV. 展开更多
关键词 Autonomous underwater vehicle(AUV) Trajectory tracking Neural networks Backstepping method Terminal sliding mode Adaptive control
暂未订购 下载PDF
Backstepping sliding mode control for uncertain strict-feedback nonlinear systems using neural-network-based adaptive gain scheduling 认领 引用 被引量：16
4
作者 YANG Yueneng YAN Ye 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第3期580-586,共7页
A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain st... A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control（SMC） law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network（RBFNN） is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network（NN）, with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC. 展开更多
关键词 backstepping control sliding mode control（SMC） neural network（NN） strict-feedback system chattering decrease
暂未订购 下载PDF
Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect 认领 引用 被引量：10
5
作者 Ming Yue Linjiu Wang Teng Ma 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第3期498-506,共9页
Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neura... Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment. 展开更多
关键词 Ground friction radial basis function(RBF) neural network(NN) slippage effect terminal sliding mode control(TSMC) wheeled mobile robot(WMR)
暂未订购 下载PDF
Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems 认领 引用 被引量：7
6
作者 Dianwei Qian Guoliang Fan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第3期706-717,共12页
This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turb... This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme. 展开更多
关键词 Generation rate constraint(GRC) load frequency control(LFC) radial basis function neural networks(RBF NNs) renewable power system terminal sliding mode control(T-SMC)
暂未订购 下载PDF
Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks 认领 引用 被引量：14
7
作者 Hong-Jun Yang Min Tan 《International Journal of Automation and computing》 CSCD 2018年第2期239-248,共10页
This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function （RBF） neural network. The flexible manipulator in this paper i... This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function （RBF） neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation （PDE） model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov＇s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations. 展开更多
关键词 Sliding mode control adaptive control neural network flexible manipulator partial differential equation （PDE）.
暂未订购 下载PDF
An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems 认领 引用 被引量：1
8
作者 刘子龙 田方 张伟军 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第6期753-758,共6页
A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that dece... A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller. 展开更多
关键词 BP neural networks sliding mode control large-scale nonlinear systems uncertainty dynamics
暂未订购 下载PDF
Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV 认领 引用 被引量：2
9
作者 Qiao Guanyu Peng Cheng 《High Technology Letters》 EI CAS 2018年第2期142-148,共7页
The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied.Firstly,a dynamical and kinematica... The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied.Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed.Considering model uncertainties and external disturbances,a robust backstepping sliding mode control(BSMC)with self recurrent wavelet neural network(SRWNN)method is proposed as the attitude controller for the coaxial twelve-rotor.A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers.SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem.Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments. 展开更多
关键词 coaxial twelve-rotor UAV backstepping sliding mode control(BSMC) self re-current wavelet neural network(SRWNN) model uncertainties external disturbances
暂未订购 下载PDF
Adaptive neural network based sliding mode altitude control for a quadrotor UAV 认领 引用 被引量：3
10
作者 Hadi RAZMI 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第11期2654-2663,共10页
Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the ... Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results. 展开更多
关键词 adaptive sliding mode controller analog neural network(ANN) altitude control of quadrotor parametric uncertainty
暂未订购 下载PDF
Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm 认领 引用 被引量：6
11
作者 Imen Zaidi Mohamed Chtourou Mohamed Djemel 《International Journal of Automation and computing》 CSCD 2019年第2期213-225,共13页
This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the ... This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model(DNM)is used to learn the behavior of the system, then, an inverse neural model(INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation(BP) algorithm. In this work, the sliding mode-backpropagation(SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy. 展开更多
关键词 Discrete time uncertain nonlinear systems neural modelling sliding mode backpropagation (BP) algorithm robust neural control
暂未订购 下载PDF
Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control 认领 引用 被引量：2
12
作者 喜彦贵 于永光 +1 位作者 张硕 海旭东 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第1期223-227,共5页
The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural n... The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method. 展开更多
关键词 fractional-order neural networks finite-time sliding mode control parameters estimation
暂未订购 下载PDF
Neural network based adaptive sliding mode control of uncertain nonlinear systems 认领 引用 被引量：5
13
作者 Ghania Debbache Noureddine Goléa 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第1期119-128,共10页
The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activat... The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control （SMC）. Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results. 展开更多
关键词 nonlinear system neural network sliding mode con- trol （SMC） adaptive control stability robustness.
暂未订购 下载PDF
Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network 认领 引用 被引量：5
14
作者 Vishal Goyal Vinay Kumar Deolia Tripti Nath Sharma 《Intelligent Control and Automation》 2015年第1期75-83,共9页
This paper presents a robust sliding mode controller for a class of unknown nonlinear discrete-time systems in the presence of fixed time delay. A neural-network approximation and the Lyapunov-Krasovskii functional th... This paper presents a robust sliding mode controller for a class of unknown nonlinear discrete-time systems in the presence of fixed time delay. A neural-network approximation and the Lyapunov-Krasovskii functional theory into the sliding-mode technique is used and a neural-network based sliding mode control scheme is proposed. Because of the novality of Chebyshev Neural Networks (CNNs), that it requires much less computation time as compare to multi layer neural network (MLNN), is preferred to approximate the unknown system functions. By means of linear matrix inequalities, a sufficient condition is derived to ensure the asymptotic stability such that the sliding mode dynamics is restricted to the defined sliding surface. The proposed sliding mode control technique guarantees the system state trajectory to the designed sliding surface. Finally, simulation results illustrate the main characteristics and performance of the proposed approach. 展开更多
关键词 Discrete-Time Nonlinear Systems Lyapunov-Krasovskii Functional Linear Matrix Inequality (LMI) Sliding Mode Control (SMC) Chebyshev Neural Networks (CNNs)
暂未订购 下载PDF
Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV 认领 引用 被引量：14
15
作者 Cheng Peng Yue Bai +3 位作者 Xun Gong Qingjia Gao Changjun Zhao Yantao Tian 《IEEE/CAA Journal of Automatica Sinica》 EI 2015年第1期56-64,共9页
This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV.... This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances. © 2014 Chinese Association of Automation. 展开更多
关键词 Adaptive control systems Aircraft control Approximation algorithms Attitude control Backstepping Controllers Functions Learning algorithms Radial basis function networks Robust control Robustness (control systems) Sliding mode control Uncertainty analysis
暂未订购 下载PDF
Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer 认领 引用 被引量：10
16
作者 Wenxue Chen Yudong Hu +1 位作者 Changsheng Gao Ruoming An 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第2期412-429,共18页
This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance system... This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots. 展开更多
关键词 BP network neural Integral sliding mode control(ISMC) Missile defense Prescribed performance function(PPF) State observer Tracking guidance system
暂未订购 下载PDF
Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network 认领 引用 被引量：2
17
作者 Jiahui Liu Nan Guo +4 位作者 Yixin Peng Wenlu Li Junfei Qiao Xiaolong Gao Wei Xiong 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第12期318-326,共9页
Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares.Due to the complex and variable composition of the waste gas they handle,the combustion environment is severely... Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares.Due to the complex and variable composition of the waste gas they handle,the combustion environment is severely affected by meteorological conditions.Key process parameters such as intake composition,flow rate,and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability.As a result,the control methods for these flares are limited,leading to poor control effectiveness.To address this issue,this paper proposes an adaptive sliding mode control method based on the radial basis function(RBF)network.Firstly,the operational characteristics of the petrochemical flare combustion process are analyzed,and a control model for the combustion process is established based on carbon dioxide detection.Secondly,an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system.Finally,by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law,fast and stable control of the flare combustion state is achieved.Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state,and the adaptive law also accomplishes system identification. 展开更多
关键词 Petrochemical torch combustion process Sliding mode control RBF neural network System identification Fast response
暂未订购 下载PDF
Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm 认领 引用 被引量：5
18
作者 Mahdi Chavoshian Mostafa Taghizadeh Mahmood Mazare 《International Journal of Automation and computing》 CSCD 2020年第3期428-438,共11页
Pneumatic artificial muscles(PAM)have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications.Since accomplishing accurate control of th... Pneumatic artificial muscles(PAM)have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications.Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior,elasticity and ambiguous characteristics,a high performance and efficient control approach should be adopted.Besides of the mentioned challenges,limited course length is another predicament with the PAM control.In this regard,this paper proposes a new hybrid dynamic neural network(DNN)and proportional integral derivative(PID)controller for the position of the PAM.In order to enhance the proficiency of the controller,the problem under study is designed in the form of an optimization trend.Considering the potential of particle swarm optimization,it has been applied to optimally tune the PID-DNN parameters.To verify the performance of the proposed controller,it has been implemented on a real-time system and compared to a conventional sliding mode controller.Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM. 展开更多
关键词 Dynamic neural network(DNN)control hybrid control pneumatic muscle particle swarm optimization sliding mode control
暂未订购 下载PDF
Adaptive RBF network based sliding mode controller with novel switching term for hovering deep sea mining vehicle trajectory tracking 认领 引用
19
作者 Jialuan Xiao Zida Shan +6 位作者 Junjun Cao Yanjie Sun Yang Cao Xinguang Du Baoheng Yao Caoyang Yu He Zhang 《Journal of Ocean Engineering and Science》 SCIE CSCD 2026年第1期13-32,共20页
Hovering deep-sea mining provides a new direction for deep-sea nodule mining;however,accurate trajectory tracking of hovering mining vehicle remains a key challenge.This paper presents an adaptive RBF network sliding ... Hovering deep-sea mining provides a new direction for deep-sea nodule mining;however,accurate trajectory tracking of hovering mining vehicle remains a key challenge.This paper presents an adaptive RBF network sliding mode controller(ARBFNNSMC)for trajectory tracking of hovering mining vehicles.The ARBFNNSMC control loop features an RBF network to compensate for lumped external disturbance and system uncertainty,online adjustment of the RBF network parameters are achieved via adaptive laws derived based on Lyapunov function to guarantee closed-loop stability.In addition,a new type of smooth switching term is proposed on the basis of the plate-pole capacitor model for reduced chattering and adopted in ARBFNNSMC.To assess control performance and robustness,numerical simulations were performed based on three typical hovering mining trajectories.The simulation results demonstrate that the proposed controller achieved excellent robustness in all simulation cases,attaining reduced tracking error,overshoot,and settling time with improved chattering suppression in control output.Compared to conventional sliding mode controller,the mean RMS tracking error and settling time were 30.9%and 61.8%lower,respectively,while the thrust oscillation was reduced by 57.3%. 展开更多
关键词 Deep-sea nodule mining Hovering deep-sea mining vehicle Sliding mode control Trajectory tracking Artificial neural network Adaptive control Plate-pole model
The study of film tension control system based on RBF neural network and PID 认领 引用
20
作者 Jia Chunying Ding Zhigang Chen Yuchen 《International English Education Research》 2014年第8期82-85,共4页
In the BOPP （Biaxially Oriented Polypropylene） production line, the tension size and smooth film received change volume has a decisive effect on the rolling quality, casting machine is a complicated electromechanica... In the BOPP （Biaxially Oriented Polypropylene） production line, the tension size and smooth film received change volume has a decisive effect on the rolling quality, casting machine is a complicated electromechanical control system, tension control of casting machine are the main factors that influence the production quality. Analyzed the reason and the tension control mathematical model generation casting machine tension in the BOPP production line, for the constant tension control of casting machine, put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network, combined with the incremental PID algorithm to realize the self-tuning tension control parameters, control simulation and implementation of the model using Matlab software programming. The simulation results show that, the improved algorithm has better control effect than the general PID. 展开更多
关键词 Control PID algorithm Jacobian information identification RBF neural network Matlab
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 42 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈