{"title":"The Performance Of Adaptive Feedforward And Optimal Feedback Active Control Systems","authors":"S. Elliott, M. Tsujino","doi":"10.1109/ASPAA.1991.634143","DOIUrl":null,"url":null,"abstract":"L Active control systems are often implemented as feedforward controllers, using a reference signal correlated ‘with the disturbance which is to be controlled. In many cases the disturbance is periodic. If the field to be controlled were perfectly stationary, a fixed, timeinvariant, feedforward controller could be implemented, which could be designed beforehand to give optimal reductions. In most practical situations, however, the primary disturbance is changing either in magnitude, phase, or frequency and the controller has to be made adaptive in order to track these changes. Such adaptive controllers have transient convergence propemes which, in general, it is difficult to analyse. This is because of the interaction between the dynamic behaviour of the controller and the dynamic behaviour of the physical system under control. The block diagram of a single channel adaptive feedforward controller is shown in Figure 1. Typically, the controller is implemented as an FIR digital filter and the algorithm used to adjust the filter coefficients is the fdtered-x LMS algorithm widrow and Steams, 19851, for which there is a multichannel generation known as the Multiple Error LMS algorithm Flliott et al., 19871. If it is assumed that the controller is adapting slowly compared with the delays and time coristants of the system under control, fairly conventimal methods can be used to analyse this algorithm, which are similar to those used in the analysis of the electrical LMS algorithm PVidrow and Stems, 19851. It has been observed, however, that in practice the filtered-x LMS irlgorithm is able to adapt much faster than this.","PeriodicalId":146017,"journal":{"name":"Final Program and Paper Summaries 1991 IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Final Program and Paper Summaries 1991 IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASPAA.1991.634143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
L Active control systems are often implemented as feedforward controllers, using a reference signal correlated ‘with the disturbance which is to be controlled. In many cases the disturbance is periodic. If the field to be controlled were perfectly stationary, a fixed, timeinvariant, feedforward controller could be implemented, which could be designed beforehand to give optimal reductions. In most practical situations, however, the primary disturbance is changing either in magnitude, phase, or frequency and the controller has to be made adaptive in order to track these changes. Such adaptive controllers have transient convergence propemes which, in general, it is difficult to analyse. This is because of the interaction between the dynamic behaviour of the controller and the dynamic behaviour of the physical system under control. The block diagram of a single channel adaptive feedforward controller is shown in Figure 1. Typically, the controller is implemented as an FIR digital filter and the algorithm used to adjust the filter coefficients is the fdtered-x LMS algorithm widrow and Steams, 19851, for which there is a multichannel generation known as the Multiple Error LMS algorithm Flliott et al., 19871. If it is assumed that the controller is adapting slowly compared with the delays and time coristants of the system under control, fairly conventimal methods can be used to analyse this algorithm, which are similar to those used in the analysis of the electrical LMS algorithm PVidrow and Stems, 19851. It has been observed, however, that in practice the filtered-x LMS irlgorithm is able to adapt much faster than this.
主动控制系统通常被实现为前馈控制器,使用与被控制扰动相关的参考信号。在许多情况下,这种干扰是周期性的。如果要控制的场是完全静止的,则可以实现一个固定的、时不变的前馈控制器,该控制器可以事先设计以给出最佳的缩减。然而,在大多数实际情况下,主要干扰的大小、相位或频率都在变化,为了跟踪这些变化,控制器必须自适应。这种自适应控制器具有暂态收敛性,一般情况下难以分析。这是因为控制器的动态行为和被控制的物理系统的动态行为之间的相互作用。单通道自适应前馈控制器框图如图1所示。通常,控制器被实现为FIR数字滤波器,用于调整滤波器系数的算法是fdterd -x LMS算法(widrow and Steams, 19851),其中有一个多通道生成称为多误差LMS算法(Flliott et al., 19871)。如果假设控制器与被控系统的延迟和时间常数相比自适应较慢,则可以使用相当常规的方法来分析该算法,类似于分析电LMS算法(PVidrow and stem, 19851)时使用的方法。然而,已经观察到,在实践中,滤过的x LMS算法能够比这更快地适应。