Neural network-based control for the fiber placement composite manufacturing process

At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. Work supported by the McDonnell Douglas Independent Research and Development Program Export Authority: 22CFR 125.4(b)(13)     

基于小脑模型神经网络控制的混合驱动连杆机构控制系统研究

混合驱动连杆机构系统是一种新型的可控机构,采用一个不可控电机和一个实时可控电机作为驱动元.两种类型的输入运动通过一个两自由度运行机构复合后,可得到柔性输出运动.位置控制系统对保证混合驱动连杆机构输出精度起着决定性的作用,其控制系统要求实现高精度的跟随运动.文章运用复合控制思想,基于小脑神经网络,设计了混合驱动性杆机构的复合控制系统,并进行了仿真实验.仿真结果表明,该方法可以显著提高系统精度,且具有良好的控制性能.     

小脑模型神经网络控制器在天线跟踪指向控制中的应用

分析了天线跟踪指向控制系统的特点和影响系统跟踪性能的主要因素,建立了系统数学模型。根据系统特点,给出了小脑模型神经网络控制器(CMAC)和PID相结合的复合自适应控制方法的设计过程,并分析了该算法适用于天线跟踪指向控制系统的优势。该方法利用传统的PID控制,结合CMAC神经网络算法的快速自学习、精确逼近的优点,既满足了天线跟踪指向控制系统快速实时跟踪的要求,又提高了跟踪精度和跟踪平稳性。仿真结果表明,该方法对摩擦力矩有很好的抑制作用,跟踪精度较单独采用PID控制提高一个数量级,且输出稳定,具有较强的鲁棒性。     

机器人装配作业主被动复合柔顺的CMAC系统

文章研制了一维搜索手腕,提出了一种称为"一维搜索"的机器人装配作业主被动复合柔顺策略,将主动搜索自由度降低到一维,同时最大限度的利用被动柔顺.设计了以装配力信号为输入的小脑模型神经网络,建立了机器人复合柔顺装配作业的CMAC系统,收到良好的装配效果.     

活套多变量系统建模与智能解耦控制研究

活套系统控制的好坏,直接影响成品带钢的宽度、厚度等性能指标。活套系统的控制目标是恒定活套量下保持张力波动尽可能小,即降低活套张力控制和高度控制的相互干扰,使系统更加稳定。控制系统能够根据检测到的活套辊摆角的变化,一方面给出控制率去调整上游机架的轧辊速度以保持套量不变;另一方面在带钢轧制过程中,为了保持恒定带钢张力,活套输出的张力矩就必须随着活套角度的不同而相应地改变。针对活套系统非线性、强耦合、多约束等特点,以鞍钢1700热连轧精轧机组为背景,建立了活套系统的动态数学模型。针对该模型提出了一种新型控制策略--单神经元PID与小脑模型复合控制算法来实现活套系统的多变量解耦控制。仿真结果验证了本算法的有效性,其解耦响应速度和抗干扰能力明显优于传统PID解耦控制效果。     

基于CMAC的无人机紧密编队飞行控制研究

针对多无人机紧密编队飞行控制系统,提出一种基于小脑模型神经网络的编队飞行队形保持控制器。该控制器以飞行控制系统横向、纵向及垂直方向通道的动态误差作为小脑模型关节控制器（CMAC）的激励信号,并与常规的PID控制器相结合构成系统的复合控制。仿真结果表明：该控制器能够控制无人机编队,在定常运动和机动过程中都可以保持期望队形,且这种控制方法具有超调量较小,鲁棒性强,响应速度快,抗干扰能力强等优点。     

基于控制器与辨识器的转台系统设计

为改善三轴转台系统性能.结合传统控制方法与神经网络控制,提出一种基于RBF辨识转台系统的CMAC神经网络与PID并行的复合控制算法.算法采用RBF辨识对象模型,CMAC实现前馈控制,并实现PID控制参数的在线整定和优化.也给出了CMAC控制器算法和系统辨识的RBF网络算法.以某转台模型为对象,仿真结果表明算法具有了传统控制的优点,进一步也证明了算法的可行性和优越性,且具有了更强的适应性和鲁棒性,能更为有效地应用于转台系统中.     

The learning convergence of CMAC in cyclic learning

In this paper we discuss the learning convergence of the cerebellar model articulation controller (CMAC) in cyclic learning.We prove the following results.First,if the training samples are noiseless,the training algorithm converges if and only if the learning rate is chosen from (0,2).Second,when the training samples have noises,the learning algorithm will converge with a probability of one if the learning rate is dynamically decreased.Third,in the case with noises,with a small but fixed learning rate ε the mean square error of the weight sequences generated by the CMAC learning algorithm will be bounded by O(ε).Some simulation experiments are carried out to test these results.     

Design of intelligent power controller for DC–DC converters using CMAC neural network

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L ₂ tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.     

A novel associative memory approach to speech enhancement in a vehicular environment

Numerous attempts have been undertaken to apply the spectral subtraction method to cancel noise perturbations but these efforts have yet to produce an algorithm that is able to adapt well to the environmental changes in the perturbations. In addition, the variants of the spectral subtraction method so far proposed in the literature would require a non-voice activity detector (NVAD), for a single microphone system, to store the perturbation. This is used as an estimate for the reference signal. Inaccuracy in the perturbation estimates causes the cleaned speech to be corrupted by musical artifacts, which is unacceptable. Post processing of signals corrupted by the musical artifacts is very costly. This paper provides an alternative approach that employs associative memory for speech enhancement. Extensive comparison is made using the soft computing approaches for noise cancellation based on associative memories. A set of stereo microphones captures the corrupted speech in a vehicle and is used to point to the closest associative memory location. The Wiener filter approach is used to cancel the noise. The paper discusses novel examples of the associative memories using the cerebellum model for noise modeling. Experimental results show the potential of these novel soft computing architectures in generating and adapting the required Weiner filters to cancel perturbation even at signal to noise ratio (SNR) of less than −13 dB.