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首页> 外文期刊>Mechanical systems and signal processing >Tracking control of nonlinear lumped mechanical continuous-time systems: A model-based iterative learning approach
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Tracking control of nonlinear lumped mechanical continuous-time systems: A model-based iterative learning approach

机译：非线性集总机械连续时间系统的跟踪控制：基于模型的迭代学习方法

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This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.
机译：本文提出了一种基于非线性模型的迭代学习控制程序，以实现对非线性集总机械连续时间系统的精确跟踪控制。此迭代学习控制过程中使用的模型结构是新的，并结合了线性状态空间模型和非线性特征空间变换。提出了一种直观的两步迭代算法来识别模型参数。它在线性和非线性模型部分的估计之间交替。假设除了输入和输出信号外，系统的完整状态向量还可用于识别。提出了一种测量和信号处理程序，用于估计集总机械系统的这些信号。迭代学习控制过程依赖于生成给定模型输出的输入的计算，即所谓的离线模型反转。提出了一种基于牛顿法的连续时间，非线性时不变状态空间模型的离线非线性模型反演方法，并将其应用于新的模型结构。该模型反演方法不限于最小相位模型。它仅需要计算状态空间模型的一阶导数，并且适用于多变量模型。对于周期性参考信号，该方法在频域中产生紧凑的实现方式。此外，示出了当在迭代学习控制过程中使用这种反转方法时，可以指定允许学习的最大带宽。提出了与液压试验台上的四分之一汽车相对应的非线性单输入单输出非线性系统的实验结果。结果表明，新的非线性方法优于线性迭代学习控制方法，该方法目前在汽车工业中用于耐久性试验台。

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