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On a new approach to model reference adaptive control.

机译:关于模型参考自适应控制的新方法。

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摘要

The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters.;Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [20]. In this new approach, instead of estimating plant parameters, the ideal control signal (what the control signal would be if the plant parameters and states were measurable) is estimated.;The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [20] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree.;Here we extend this work in several directions: (i) In [20], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size. (ii) A key drawback of the controller of [20] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem. (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant. (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability. (v) A number of approaches to improve the noise behaviour of the controller are presented.
机译:自适应控制的目的是设计一种控制器,该控制器可以调整其行为以容许不确定的或随时间变化的参数。自适应控制器通常由线性时不变(LTI)补偿器以及调整该补偿器参数并产生非线性控制器的调整机制组成。由于非线性,瞬态闭环行为通常很差,并且控制信号可能变得过大。尽管自适应控制的最初目标是处理时变的工厂参数,但是大多数经典的自适应控制器无法处理快速变化的参数。;最近,已提出使用线性周期(LP)控制器作为该领域的一种新方法模型参考自适应控制[20]。在这种新方法中,不是估算工厂参数,而是估算理想的控制信号(如果工厂参数和状态是可测量的,则控制信号将是什么)。所得到的控制器具有许多理想的功能:(1)它可以处理工厂参数的快速变化;(2)它提供了闭环系统的良好瞬态行为;(3)确保初始条件的影响呈指数下降到零;(4)它产生适度的控制信号在尺寸方面。尽管线性周期控制器(LPC)具有上述优点,但存在一些缺陷。为了获得期望的特征,使用了快速变化的控制信号和较小的采样周期。快速时变的控制信号需要快速的执行器,这可能不实用。 LPC [20]的第二个缺点是噪声抑制性能差。由于在跟踪和噪声容限之间存在明显的权衡关系,因此较小的采样周期会导致较大的控制器增益并相应降低噪声灵敏度。作为最后一个缺点,该控制器需要了解确切的植物相对程度。在此,我们将工作扩展到多个方向:(i)在[20]中,无穷范数用于测量信号大小。在这里,我们重新设计了控制器,以产生一个新版本,当使用更常见的2范数来测量信号大小时,该版本可以提供可比的结果。 (ii)[20]的控制器的主要缺点是控制信号快速移动。在这里,我们重新设计了控制律,以大大减轻这一问题。 (iii)重新设计的控制器可以处理较大的参数变化,并且在已知高频增益的符号的情况下,闭环系统具有显着的噪声容限。 (iv)我们证明,在一个重要的特殊情况下,至少从提供稳定性的角度来看,我们可以用相对程度的上限代替确切相对程度的知识要求。 (v)提出了许多改善控制器噪声行为的方法。

著录项

  • 作者

    Mansouri, Naghmeh.;

  • 作者单位

    University of Waterloo (Canada).;

  • 授予单位 University of Waterloo (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 212 p.
  • 总页数 212
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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