Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network

The length of fexible manipulators with a telescopic arm alters during movement.The dynamic parameters of telescopic fexible manipulators exhibit signifcant time-varying characteristics owing to variations in length.With an increase in the manipulators’length,the nonlinear terms caused by fexibility in the manipulators’dynamic equations cannot be ignored.The time-varying characteristics and nonlinear terms of telescopic fexible manipulators cause fuctuations in rotation angles,which afect the operation accuracy of end-efectors.In this study,a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of fexible telescopic manipulators.First,the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle,and the infuence of nonlinear terms is analyzed.Subsequently,a combined control strategy is proposed to suppress the fuctuation of the rotation angle in telescopic fexible manipulators.The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators.Fuzzy rules are utilized to adjust the controller parameters in real-time.The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the fexible manipulators.The uncertain part comprises time-varying parameters and nonlinear terms.Finally,numerical simulations and prototype experiments prove the efectiveness of the combined control strategy.The results prove that the proposed control strategy has a smaller standard deviation of errors.Therefore,the combined control strategy is more suitable for telescopic fexible manipulators,which can efectively improve the control accuracy of rotation angles.

Adaptive Control of Flexible Redundant Manipulators Using Neural Networks

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.

A novel cascaded H-bridge photovoltaic inverter with flexible arc suppression function

This paper presents a novel approach that simultaneously enables photovoltaic(PV)inversion and flexible arc suppression during single-phase grounding faults.Inverters compensate for ground currents through an arc-elimination function,while outputting a PV direct current(DC)power supply.This method effectively reduces the residual grounding current.To reduce the dependence of the arc-suppression performance on accurate compensation current-injection models,an adaptive fuzzy neural network imitating a sliding mode controller was designed.An online adaptive adjustment law for network parameters was developed,based on the Lyapunov stability theorem,to improve the robustness of the inverter to fault and connection locations.Furthermore,a new arc-suppression control exit strategy is proposed to allow a zerosequence voltage amplitude to quickly and smoothly track a target value by controlling the nonlinear decrease in current and reducing the regulation time.Simulation results showed that the proposed method can effectively achieve fast arc suppression and reduce the fault impact current in single-phase grounding faults.Compared to other methods,the proposed method can generate a lower residual grounding current and maintain good arc-suppression performance under different transition resistances and fault locations.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

Neural adaptive attitude tracking controller for flexible spacecraft

In this paper,a neural network adaptive controller is proposed for attitude tracking of flexible spacecraft in presence of unknown inertial matrix and external disturbance.In this approach,neural network technique is employed to approximate the unknown system dynamics with finite combinations of some basis functions,and a robust controller is also designed to attenuate the effect of approximation error,more specially,the knowledge of angular velocity is not required.In the closed-loop system,Lyapunov stability analysis shows that the attitude trajectories asymptotically follow the reference output trajectories.Finally,simulation results are presented for the attitude tracking of a flexible spacecraft to show the excellent performance of the proposed controller and illustrate its robustness in face of external disturbances and unknown dynamics.

基于改进FNN-CCC的双伺服压力机同步控制策略研究

目的改善双伺服压力机同步控制策略的动态响应性能和鲁棒性,提升双伺服压力机的单轴跟踪精度和双轴同步精度,实现成形过程的高精度位置控制。方法建立双伺服压力机驱动系统数学模型,分析系统同步误差来源,结合模糊神经网络单轴控制算法,引入迭代学习律,设计一种改进模糊神经网络-交叉耦合(FNN-CCC)同步控制器。基于系统控制模型进行单轴阶跃响应特性与双轴正弦跟随特性仿真,搭建嵌入式双伺服压力机驱动系统试验平台,在偏载干扰条件下进行双轴同步控制试验,验证所提出理论的有效性。结果仿真结果表明,与模糊控制算法和BP神经网络控制算法相比,该控制器单轴控制算法的超调量分别减少了11.5%和25.5%,调节时间分别减少了48.8%和34.4%,具有更好的动态响应性能。与原控制器相比,改进后的交叉耦合同步控制器最大双轴同步误差降低了65.7%,同步控制精度有所提高。试验结果表明,与传统PID-交叉耦合控制器相比,改进的FNN-CCC控制器有更好的控制性能,在热冲压合模成形阶段,单轴跟踪误差分别减小了81.8%和75.0%,双轴同步误差减小了69.2%。结论所提出的同步控制策略在偏载干扰条件下具有较好的动态响应性能和鲁棒性,能够使同步误差快速收敛,提高了双伺服压力机驱动系统的单轴跟踪精度和双轴同步控制精度,实现了对双伺服压力机的高精度控制。

基于递归径向基神经网络滑模的多功能柔性多状态开关控制方法

近年来,新能源和电动汽车的渗透比例逐渐增高,给配电网的潮流优化和电能质量治理带来严峻挑战。针对分布式电源的随机性和间歇性问题,设计一种基于递归径向基神经网络(RRBFNN)滑模的多功能柔性多状态开关(FMS)控制方法,在实现功率交互和多端单相接地故障柔性消弧的同时,增强FMS的抗扰能力。首先考虑扰动的影响,设计一种改进RRBFNN滑模控制方法,以克服传统滑模控制固有的抖振现象和对系统精确数学模型的依赖,并减小并网暂态冲击;柔性消弧控制采用微积分型滑模面,理论推导出0轴电压控制律,提高故障电流抑制率;进一步通过李雅普诺夫定理证明所设计方法的稳定性和收敛性。最后,在Matlab/Simulink中搭建三端口FMS及其控制系统的仿真模型,通过对比仿真验证了所提策略的可行性和有效性。

基于视觉反馈的柔性充气仿生臂控制系统设计

设计了一种基于视觉反馈的柔性仿生臂控制系统,通过将其等效为刚体的建模方法对所设计的柔性仿生臂进行了理论分析。利用手动操纵杆实现了对柔性仿生臂的手动操控。在柔性仿生臂头端安装有摄像头,可以通过卷积神经网络目标检测和识别算法识别目标物体,其获得的位置信息可用于闭环控制。通过视觉反馈实现了对目标的搜索和锁定。相较于传统的刚性机械臂,本研究具有安全性高和体积小的优点。同时,计算机仿真和实验均验证了该设计的合理性与有效性。

基于粗糙集高速公路混沌T-S FNN控制仿真

研究基于粗糙集理论的高速公路混沌系统模糊神经网络入口匝道控制方法。针对高速公路车流量不确定性特点,提出了通过数据挖掘技术建立交通流入口匝道智能混沌控制器知识库的思想;设计了以密度、上游流量和最大李亚普诺夫指数作为输入,红灯时间作为输出的T-S模糊神经网络混沌控制器;采用粗糙集理论建立混沌控制器知识库,确定模糊神经网络控制器结构并提取模糊规则;采用模糊神经网络方法对控制器参数进行优化。仿真结果表明:采用该方法设计的智能混沌控制器,可实现保持高速公路有序运动、避免交通堵塞、提高交通通行能力的目的,是提高高速公路管理控制水平的有效方法。

基于FNN解耦纸张定量水分控制策略的研究与应用

针对纸张抄造过程中纸张定量与水分之间存在强耦合的问题,提出一种模糊神经网络(Fuzzy Neural Network,FNN)的解耦控制器,首先利用模糊控制对控制系统进行耦合补偿,然后利用神经网络的自学习、自调整能力不断在控制过程中优化模糊控制规则及解耦补偿参数,成功地将纸张抄造过程的多变量系统转变为单变量系统,实现纸张定量、水分之间的解耦。仿真结果表明,采用FNN解耦控制器具有较好的动态响应和较强的鲁棒性。将该策略应用于国内某造纸厂的纸板机控制系统,纸张定量控制精度为±3.9 g/m^2左右,水分控制精度为±1.0%左右,满足该纸机定量水分高精度控制要求。

基于粒子群算法的开关磁阻电机控制系统研究

针对开关磁阻电机本身所具有的强非线性,转矩脉动大、调速性能待改进等问题,采用了一种基于粒子群优化模糊PI控制并结合柔性神经网络的控制方法,将模糊控制理论与PI控制相结合,并通过粒子群算法进行优化。进行了速度外环为无优化模糊PI控制、传统PID控制以及粒子群优化模糊PI控制三种仿真实验,三种仿真实验均采用柔性神经网络作为转矩内环。对比仿真结果发现,粒子群优化模糊PI控制在额定转速为150 r/min、800 r/min、1500 r/min三种情况下的转矩脉动最小,提高了系统的鲁棒性,对比实验表明,粒子群优化的模糊PI控制作为速度外环,柔性神经网络作用于转矩内环的控制方法性能优良,适用于开关磁阻电机的转矩脉动抑制。

列主元SVD-QR方法修剪策略参数调整的D-FNN算法研究

针对动态模糊神经网络,提出了列主元SVD-QR方法修剪策略与参数调整的新算法。其中采用列主元SVD-QR方法修剪策略从给定的规则库中提取最重要模糊规则,使得网络结构不会持续增长,避免了过拟合及过训练现象;采用扩展的卡尔曼滤波方法把全局算法划分成线性和非线性部分,线性和非线性参数可以分别被更新,从而可以达到快速的学习速度。通过对血压的控制来验证所提出算法的有效性,结果证明了列主元SVD-QR方法修剪策略参数调整的D-FNN算法具有良好的性能。

基于RBFNN整定的PID控制在柔性摆跟踪控制中的应用研究

本文提出了一种基于径向基函数神经网络(RBFNN)整定的PID控制策略,并将其应用于柔性倒立摆的跟踪控制。该方法通过神经网络辨识获取柔性摆的Jacobian信息,采用梯度下降法自适应调整PID的控制参数。仿真结果表明,与传统的PID控制效果相比,该控制方法响应速度快、超调量小,较好地解决了PID控制方法中参数整定困难的问题,实现了对柔性摆的有效跟踪控制。

基于事件触发机制和最小学习参数的FAHV指定时间收敛自适应控制

针对当前吸气式高超声速飞行器自适应控制结果仅能实现误差渐近收敛于预设包络、神经权值在线更新存在计算爆炸、对机载资源过度占用的难题,提出了基于事件触发机制和最小学习参数的FAHV指定时间收敛自适应控制方法。首先,阐述了一种不依赖精确误差初值同时又能确保误差指定时间收敛的改进预设性能控制机制;其次,构建了用于FAHV干扰辨识的相对阈值事件触发神经网络;最后,设计了相对阈值事件触发控制算法,有效降低了闭环控制器对通信资源的消耗,在非等周期信号传输的基础上实现了良好的控制精度。仿真结果表明,所提方法能够在低计算与传输资源消耗下对高度/速度参考信号实施指定时间跟踪。

基于智能控制的燃煤锅炉灵活调峰技术

[目的]随着对火电机组灵活调峰的需求骤增及信息技术的飞速发展,火电企业正由传统运行及管理模式,向更加清洁、高效、可靠的数字化及智能化方向发展,如何提高燃煤机组灵活调峰过程中的智能化、精准化及可靠性,已成为行业内人员所关注的重点问题。[方法]文章针对深度调峰的背景需求,对燃煤锅炉灵活调峰中的典型问题进行总结,分析了应用于锅炉的控制理论整体发展状况。基于此,分别从锅炉燃烧性能优化控制、宽负荷脱硝精准控制、锅炉运行能效控制、主辅机设备监测及诊断等4个方面对基于智能控制的燃煤锅炉灵活调峰算法模型及应用进行了综合分析。[结果]在此基础上,详细探讨了智能控制理论及模型在燃煤锅炉灵活调峰中的研究进展及应用效果。[结论]目前,基于燃煤锅炉灵活调峰过程中的常见问题,需要在燃烧组织方式稳燃能力深度提升的基础上,提高基础数据获取准确性,促进数据及知识的互补融合,加强多目标优化控制及DCS控制系统优化等,同时兼顾调峰经济性与机组寿命之间的辩证优化关系,从而为提高燃煤锅炉灵活调峰能力提供智能化及精准化解决方案。

基于PFNN的火电机组过热汽温辨识方法及控制策略

提出一种基于机理与数据混合的深度神经网络(PFNN)辨识方法,用于过热器温度被控对象的多变量回归建模,进而发展深度神经网络(DNN)动态前馈控制策略,抑制过热器温度的频繁波动.PFNN辨识方法融合过热器动态机理与长短期记忆神经网络,对强耦合、大时延的过热器温度对象具有很强的泛化能力和稳定性,可实现动态特性的精确预测.结果表明,机组灵活调峰时,过热器导前区温度和滞后区温度的PFNN辨识结果与现场实际温度的平均绝对偏差在1℃以内,模型可靠性较高.采用DNN动态前馈控制后,过热器出口蒸汽温度与设定值偏差维持在±2.5℃以内,有效抑制了因负荷变化引起的过热器蒸汽出口温度波动.

基于滑膜控制和SAFNN的鲁棒混沌同步

针对带有干扰的混沌系统,进行了基于自组织自适应模糊神经网络(SAFNN)的同步控制器的设计,采用基于聚类划分的模糊神经网络,推导出模糊神经网络参数在线更新算法,并设计了神经网络结构自适应算法,从隶属度函数的生长、模糊规则的分裂和模糊规则的修剪三个方面动态调整模糊神经网络结构,摒弃了常规的试错法来调整系统的结构和参数,以便更好地满足对计算负荷和控制性能的要求,实现两者之间的平衡;最后结合滑模控制器和鲁棒控制器,对带有干扰的混沌系统进行同步控制仿真,仿真结果表明该方法有效。

Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

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.

VIBRATION SUPPRESSION OF A FLEXIBLE PIEZOELECTRIC BEAM USING BP NEURAL NETWORK CONTROLLER

This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.

Robust dynamic surface control of flexible joint robots using recurrent neural networks

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H^infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dy- namic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally. simulation results verifv the effectiveness of the nronosed control scheme.