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伺服系统在摩擦条件下的模拟复合正交神经网络控制 总被引:3,自引:3,他引:3
叶军 《中国电机工程学报》2005,25(17):0-130
在数字复合正交神经网络的基础上提出一种模拟复合正交神经网络,并用于非线性伺服系统控制中.在带有非线性摩擦力矩的直流电机飞行模拟转台伺服系统中,控制系统是基于PD控制加神经网络前馈控制的并行控制方法,使用神经网络是用来消除非线性摩擦力矩的影响.通过数字复合正交神经网络的连续化算法处理获得了一种模拟复合正交神经网络,并作为前馈控制器.用并行控制与单一的PD控制对带有非线性摩擦力矩的直流电机伺服控制作了仿真研究.仿真结果表明复合控制比单一的PD控制具有实时性好、响应速度快、跟踪精度高,位置与速度跟踪控制获得了满意的效果.该模拟神经控制器能用于不确定对象的控制,为不确定系统控制提供了一种新的途径. 相似文献
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Kenji Kurosawa Ryoko Futami Takashi Watanabe Nozomu Hoshimiya 《IEEE transactions on neural systems and rehabilitation engineering》2005,13(3):359-371
The feedback error learning (FEL) scheme was studied for a functional electrical stimulation (FES) controller. This FEL controller was a hybrid regulator with a feedforward and a feedback controller. The feedforward controller learned the inverse dynamics of a controlled object from feedback controller outputs while control. A four-layered neural network and the proportional-integral-derivative (PID) controller were used for each controller. The palmar/dorsi-flexion angle of the wrist was controlled in both computer simulation and FES experiments. Some controller parameters, such as the learning speed coefficient and the number of neurons, were determined in simulation using an artificial forward model of the wrist. The forward model was prepared by using a neural network that can imitate responses of subject's wrist to electrical stimulation. Then, six able-bodied subjects' wrist was controlled with the FEL controller by delivering stimuli to one antagonistic muscle pair. Results showed that the FEL controller functioned as expected and performed better than the conventional PID controller adjusted by the Chien, Hrones and Reswick method for a fast movement with the cycle period of 2 s, resulting in decrease of the average tracking error and shortened delay in the response. Furthermore, learning iteration was shortened if the feedforward controller had been trained in advance with the artificial forward model. 相似文献
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过热汽温模糊神经网络预测控制器的设计 总被引:15,自引:8,他引:15
针对锅炉过热汽温的特点,设计前馈—反馈串级复合型控制系统。主控制器采用基于神经网络预测模型的模糊神经控制,即该控制器首先是将神经网络与预测控制相结合,采用改进的递阶遗传算法对神经网络的权值和结构同时进行训练,实现了非线性、大时滞系统模型的精确预测;然后将模糊控制与神经网络相结合,实现模糊神经预测控制。副控制器采用二自由度PID控制器。仿真结果表明,该控制显著提高锅炉过热汽温这一非线性、大时滞系统的控制品质,且易于工程实现。 相似文献
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This paper addresses the problem of controlling the speed of a permanent-magnet stepper motor assumed to operate in a high-performance drives environment. An artificial neural network (ANN) control scheme which uses continual online random training (with no offline training) to simultaneously identify and adaptively control the speed of the stepper motor is proposed. The control scheme utilizes two three-layer feedforward ANNs: (1) a tracker identification neural network which captures the nonlinear dynamics of the motor over any arbitrary time interval in its range of operation; and (2) a controller neural network to provide the necessary control actions to achieve trajectory tracking of the motor speed. The inputs to the controller neural network are not constructed from the actual motor/load dynamics, but as a feedback signal, from the estimated state variables of the motor supplied by the neural identifier and the reference trajectory to be tracked by the actual speed. A full nonlinear model (with no simplifying assumptions) is used to model the motor dynamics, and to the best of the authors' knowledge this represents the first such attempt for this device. This paper also makes use of a very realistic and practical scheme to estimate and adaptively learn the noise content in the speed-load torque characteristic of the motor. Simulations reveal that the neural controller adapts and generalizes its learning rate to a wide variety of loads, in addition to providing the necessary abstraction when measurements are contaminated with noise 相似文献
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Yu-Sheng Lu Shuan-Min Lin Markus Hauschild Gerd Hirzinger 《Electrical Engineering (Archiv fur Elektrotechnik)》2013,95(4):357-365
This paper presents a scheme for controlling the output torque of a harmonic drive actuator equipped with a torque sensor. The proposed control is composed of a feedback control and a feedforward learning control, in which the feedback control shapes nominal system dynamics using the internal model control structure. The feedforward learning controller employs a disturbance observer (DOB) to evaluate compensation error of the feedforward control for the learning, so as to compensate for torque ripples induced by harmonic drives. Robust stability conditions of the proposed DOB-based learning control system are provided. Experimental results show the effectiveness of the proposed scheme in alleviating the major component of torque ripples whose frequency is twice the angular frequency of the input shaft. 相似文献
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Abraham K. Ishihara Johan van Doornik Shahar Ben‐Menahem 《International Journal of Adaptive Control and Signal Processing》2010,24(6):445-466
We consider a neural network‐based controller for a rigid serial link manipulator with uncertain plant parameters. We assume that the training signal to the network is corrupted by signal‐dependent noise. A radial basis function network is utilized in the feedforward control to approximate the unknown inverse dynamics. The weights are adaptively adjusted according to a gradient descent plus a regulation term (Narendra's e‐modification). We prove a theorem that extends the Yoshizawa D‐boundedness results to the stochastic setting. As in the deterministic setting, this result is particularly useful for neural network robot control when there exists bounded torque disturbances and neural net approximation errors over a known compact set. Using this result, we establish bounds on the feedback gains and learning rate parameters that guarantee the origin of the closed‐loop system is semi‐globally, uniformly bounded in expected value. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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文章利用模糊神经网络的模糊推理能力以及前馈神经网络的逼近能力,将其与自适应控制方案结合,并取带有控制增量约束的广义目标函数作为优化指标;从而推导出一种能对非线性非最小相位系统进行有效控制的模糊神经网络间接自适应控制器。在网络学习算法上分别采用Davidon最小二乘法和带有动量项的BP算法。仿真结果表明了该方法的有效性。 相似文献
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针对两相交错并联buck系统在受到干扰时,输出波动较大,恢复时间较长的问题,基于微分平坦理论,提出了一种平坦控制策略。设计了微分平坦控制器,控制器分为两个部分:前馈控制器和反馈补偿器,前馈控制器可以抵消系统的非线性特性,使系统跟随期望的输出轨迹;反馈补偿器用于消除因干扰和系统的未建模部分所引起的系统输出偏离期望轨迹的现象。微分平坦控制器输出电压给定值变化时,能够在更短的时间内重新跟随系统期望输出,同时在干扰产生时,有更好的抗干扰能力。该控制器在提高系统动态特性方面,有一定的参考价值。 相似文献
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在介绍模糊RBF神经网络基本原理的基础上设计了模糊RBF神经网络控制器,并将其应用于大型变速变桨风力发电机组的变桨距控制中.在风速高于额定风速时,通过控制桨叶节距角来改变攻角从而改变风机获得的空气动力转矩,以实现输出功率稳定在额定值.将该模糊神经网络控制方法和PI控制进行仿真比较,结果表明前者优于PI控制. 相似文献
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介绍了锥形滚柱式磁悬浮轴承的实验系统并利用线性控制原理对其建立了数学模型。应用神经网络和模糊控制理论设计了非线性控制器。从线性最优控制器设计值中提取经验规则,并将模糊化后的变量引入神经网络结构,然后采用变尺度算法对网络参数进行优化,解模糊后得到反馈增益矩阵。仿真结果表明控制器对静态和暂态稳定均有良好效果。 相似文献
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基于递归模糊神经网络的感应电机无速度传感器矢量控制 总被引:25,自引:16,他引:25
该文提出了一种控制性能较好的递归模糊神经网络(RFNN)无速度传感器感应电机矢量控制方法,该方法使用模型参考自适应方法辨识转子磁场位置和转速,采用递归模糊神经网络控制器作为转矩控制器来近似系统最优控制器输出。仿真实验表明,当系统参数动态变化或受到外部不确定性因素的影响时,利用神经网络来在线动态的调整网络的隶属函数参数以及神经网络递归权值,使系统仍将具有很好的动静态性能。 相似文献
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为了解决多余力矩对飞机舵机电动加载系统带来的干扰问题,提出采用结合基于结构不变性原理的前馈控制补偿器与基于改进超螺旋滑模算法的反馈控制器组成复合控制策略。在前馈控制补偿器中,通过计算多余力矩干扰比对多余力矩进行定量分析,以实现对产生干扰比较大的舵机运动干扰项进行抑制。在反馈控制器中,采用改进超螺旋滑模算法对系统转速环进行控制,通过对原算法中不连续的符号函数进行平滑处理,保证控制输入连续。仿真结果表明,这一复合控制策略不仅能够对多余力矩干扰实现有效地抑制,同时还进一步实现了系统的加载精度及控制性能的提升。 相似文献
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基于变结构理论的感应电动机模糊线性调速系统 总被引:3,自引:0,他引:3
运用变结构控制理论分析和设计感应电动机磁场定向变频调速系统的模糊速度控制器 ,并在此基础上提出了在模糊控制器的输出增加开关线的前馈项构成模糊线性复合控制的方法。理论分析和实验结果均表明 ,这种复合控制方法 ,不仅保持了模糊控制器原有的鲁棒性 ,而且提高了响应的稳态精度 ,改善了系统响应的快速性。 相似文献
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为了减小二维位置敏感探测器(2D-PSD)的非线性误差,研究了一种基于模糊神经网络的2D-PSD非线性修正算法.基于调制光源法采集到2D-PSD光敏面上可靠的光点位置信号后,采用T-S型模糊神经网络近似表示光点位置信号与光点位置坐标之间的映射,将该映射嵌入到ARM处理器中实现2D-PSD的在线修正计算.采用该模糊神经网络非线性修正算法后,2D-PSD的非线性误差从±0.4 mm减小到±0.15 mm,实验结果表明基于模糊神经网络非线性修正算法对有效性. 相似文献