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1.
针对我国风力发电机齿轮箱状态监测与故障诊断发展的实际需要,利用温度采集技术和虚拟仪器技术,专为传动系统的状态监测提供了一套应用解决方案.建立了传动系统模拟装置,模拟了风力发电机齿轮箱的工作状态.研制了基于温度信号的在线监测装置,设计了基于AD590温度传感器的多路信号采集电路,并采用Labview进行软件编程.使用基于温度信号的在线监测装置对传动系统模拟装置进行了温度信号采集,实验结果表明,在线监测装置可以实现对传动系统的在线状态监测,为实验室条件下传动系统状态监测与故障诊断技术的研究提供了有效的技术支持.  相似文献   

2.
针对人脸在跟踪过程中可能存在大幅度的倾斜、旋转、遮挡以及肤色干扰等问题,提出一种基于在线修正的人脸跟踪算法.该算法当人脸检测失效时,人脸跟踪模块将用于提取目标参数;而在人脸跟踪过程中,为减小由连续跟踪造成的累积误差,利用人脸实时检测机制新检测到的人脸目标参数来修正跟踪模块的参数,包括跟踪窗口的位置和尺度,从而利用了人脸检测和人脸跟踪各自的优点.通过实验,其结果表明,该算法能够精确地跟踪复杂姿态下的人脸目标,并且能够解决肤色干扰和遮挡的问题,具有很好的适应性和鲁棒性.另外,将在线修正的跟踪方法应用于娱乐游戏控制,为人机交互提供了新的方式.  相似文献   

3.
何永强  张启先 《机器人》2002,24(1):26-30
针对多指灵巧手钢缆传动系统的非线性,提出一种基于分散神经网络的位置控制方法.通过 对复杂的钢缆传动系统施加不同的输入可以得到特定的相对简单的输入输出数据,利用这种 特定的输入输出数据学习传动系统的非线性关系得到多个分散的神经网络,再根据传动系统 的结构特性用分散的神经网络求取钢缆传动系统的逆模型,用于直接逆控制,从而达到补偿 非线性误差的目的.同时应用在线神经网络的适时补偿使系统长时间保持良好的运行状态. 实验证明这种方法可大大提高位置跟踪精度,取得比较满意的结果.  相似文献   

4.
大型装备传动系统非线性频谱特征提取与故障诊断   总被引:1,自引:0,他引:1  
基于Volterra级数的非线性频谱分析方法,建立了大型数控装备传动系统伺服电机的非线性频谱模型,对传动系统两类参数型故障的频谱特征进行了分析.在此基础上,提出一种实用的在线频谱特征提取与故障识别方法,采用自适应辨识算法求解时域Volterra核,用快速多维傅立叶变换获得非线性频谱特征.实验结果表明,该方法实时性好,故障识别率高.  相似文献   

5.
针对纵向滑动参数未知的轮式移动机器人的轨迹跟踪问题,提出一种自适应跟踪控制策略.利用两个未知参数来描述移动机器人左右轮的纵向打滑程度,建立了产生纵向滑动的差分驱动轮式移动机器人的运动学模型;设计了补偿纵向滑动的自适应非线性反馈控制律;应用 Lyapunov 稳定性理论与 Barbalat 定理证明了闭环系统的稳定性;同时,提出了一种由极点配置方法在线调整控制器增益的方法.仿真结果验证了所提出控制方法的有效性.  相似文献   

6.
针对全方向移动机器人存在非线性动态强耦合、实时重心偏移及难以实现高精度跟踪控制的问题, 本文提 出一种基于长短期记忆(LSTM)神经网络的重心位置在线预测的轨迹跟踪控制法. 首先, 建立考虑重心偏移的动力 学模型并基于LSTM神经网络训练构建其对比模型; 其次, 基于模型对比法实时估计重心偏移参数, 再基于张神经 网络(ZNN)对估计的重心偏移参数进行预测以减小估计过程引起的滞后; 最后, 基于非线性动态反馈解耦法设计数 值加速度控制算法, 且基于离散系统极点配置法分析了系统的稳定性. 仿真结果验证了所提方法相对于数值加速 度控制器与自适应控制器因能在线预测重心偏移参数完成高精度动态解耦实现控制精度的提高. 实际实验中, 所 提控制算法相比数值加速度控制及模型预测控制, 其跟踪精度明显提高, 这表明所提控制算法可显著减小重心偏移 对跟踪控制精度的影响.  相似文献   

7.
自校正α—β—γ跟踪滤波器   总被引:1,自引:0,他引:1  
邓自立 《控制与决策》1991,6(5):384-387
对于含有未知模型参数和带未知噪声统计的一类跟踪系统,基于ARMA新息模型的在线辨识,本文提出了一种新颖的自校正α-β-γ跟踪滤波器,仿真结果说明了它的有效性。  相似文献   

8.
研究非线性系统的稳定性和跟踪优化问题,针对未知参数非线性系统的参数辨识和输出跟踪问题,给出参数自适应广义预测控制方法,为使辨识模型能实时反映被控对象特性以及输出对设定值的跟踪有较高精度.提出将非线性系统转化为受控自回归滑动平均模型,根据输入输出数据辨识模型参数.采用广义预测控制滚动优化的策略得出最优控制律,将最优控制律作用于对象实现非线性系统的优化控制以及系统输出对设定值的跟踪控制.明显克服了自适应控制对模型精度要求高的缺陷且具有在线辨识,滚动优化的特点.最后,通过仿真实例验证了方法的有效性.  相似文献   

9.
基于动态递归模糊神经网络的自适应电液位置跟踪系统   总被引:1,自引:1,他引:1  
提出了动态递归模糊神经网络(DRFNN)以在线估计电液位置跟踪系统中包括非线性、参数不确定性、负载干扰等在内的未知动态非线性函数,基于lyapunov稳定性理论推导出DRFNN可调参数和估计误差的界的自适应律,并构造出稳定的自适应控制器.实验结果表明:基于DRFNN的自适应控制器可使电液位置跟踪系统具有较强的鲁棒性和满意的跟踪性能.  相似文献   

10.
针对一类结构和参数均具备时变特性的复杂时变系统,提出一种新的基于联合滤波算法的在线自适应逆控制方法.该方法在处理参数时变问题的同时可兼顾系统的结构时变特性,实现复杂动态系统的在线跟踪控制.同时提出新的联合Volterra核函数滤波算法,该算法克服了原Volterra滤波器计算复杂运算速度慢的缺点,实现了动态非线性系统的在线跟踪控制.通过仿真分析可以得出,对于此类线性、非线性复杂时变系统,基于新的联合滤波器的自适应逆控制方法可以快速有效的实现动态对象在线建模与控制.  相似文献   

11.
In this paper, a novel control law is presented, which uses neural-network techniques to approximate the affine class nonlinear system having unknown or uncertain dynamics and noise disturbances. It adopts an adaptive control law to adjust the network parameters online and adds another control component according to H-infinity control theory to attenuate the disturbance. This control law is applied to the position tracking control of pneumatic servo systems. Simulation and experimental results show that the tracking precision and convergence speed is obviously superior to the results by using the basic BP-network controller and self-tuning adaptive controller.  相似文献   

12.
A new design approach of a multiple-input-multiple-output (MIMO) adaptive fuzzy terminal sliding-mode controller (AFTSMC) for robotic manipulators is described in this article. A terminal sliding-mode controller (TSMC) can drive system tracking error to converge to zero in finite time. The AFTSMC, incorporating the fuzzy logic controller (FLC), the TSMC, and an adaptive scheme, is designed to retain the advantages of the TSMC while reducing the chattering. The adaptive law is designed on the basis of the Lyapunov stability criterion. The self-tuning parameters are adapted online to improve the performance of the fuzzy terminal sliding-mode controller (FTSMC). Thus, it does not require detailed system parameters for the presented AFTSMC. The simulation results demonstrate that the MIMO AFTSMC can provide a reasonable tracking performance.  相似文献   

13.
This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.  相似文献   

14.
In this paper, a cerebellar-model-articulation-controller (CMAC) neural network (NN) based control system is developed for a speed-sensorless induction motor that is driven by a space-vector pulse-width modulation (SVPWM) inverter. By analyzing the CMAC NN structure and motor model in the stationary reference frame, the motor speed can be estimated through CMAC NN. The gradient-type learning algorithm is used to train the CMAC NN online in order to provide a real-time adaptive identification of the motor speed. The CMAC NN can be viewed as a speed estimator that produces the estimated speed to the speed control loop to accomplish the speed-sensorless vector control drive. The effectiveness of the proposed CMAC speed estimator is verified by experimental results in various conditions, and the performance of the proposed control system is compared with a new neural algorithm. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN.  相似文献   

15.
《Applied Soft Computing》2008,8(1):371-382
A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system.  相似文献   

16.
An adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high‐performance six‐phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field‐oriented six‐phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six‐phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six‐phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32‐bit floating‐point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.  相似文献   

17.
This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.  相似文献   

18.
多电机驱动系统是一种多输入多输出、非线性、强耦合的系统.它广泛应用在许多需要高精度协调控制的驱动领域,比如电动汽车驱动、城市轨道交通以及印刷业等.本文提出了一种新的方法用于三电机驱动系统的速度与张力的解耦控制,其核心由模糊自整定控制与BP神经网络广义逆组成.首先,由神经网络广义逆与原系统串联实现复合伪线性系统;其次,在该伪线性系统中采用模糊自整定方法.仿真结果表明:所提方法能有效实现速度与张力间的解耦,将三电机驱动系统转化为多个具有开环稳定性的单输入单输出线性子系统,同时系统的响应速度快、超调量小、瞬态时间较短,具有良好的跟踪性能,这有助于改善系统的启动特性,降低系统振荡.  相似文献   

19.
Hybrid control for speed sensorless induction motor drive   总被引:3,自引:0,他引:3  
The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results  相似文献   

20.
This paper presents a theoretical framework for adaptive control of a wind energy conversion system (WECS), involving a squirrel cage induction generator (SIG) connected with an AC/DC/AC IGBT‐based PWM converter. A multi‐loop nonlinear controller is designed to meet two main control objectives, i.e., (i) speed reference optimization in order to extract a maximum wind energy whatever the wind speed, and (ii) power factor correction (PFC) to avoid net harmonic pollution. These objectives must be achieved despite the mechanical parameters uncertainty. First, a nonlinear model of the whole controlled system is developed within the Park coordinates. Then, a multi‐loop nonlinear controller is synthesized using the adaptive backstepping design. A formal analysis based on Lyapunov stability is carried out to describe the control system performances. In addition to closed‐loop global asymptotic stability, it is proven that all control objectives (induction generator speed tracking, rotor flux regulation, DC link voltage regulation and unitary power factor) are asymptotically achieved.  相似文献   

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