Optimal Selection of Basis Functions for Robust Tracking Control of Uncertain Linear Systems—With Application to Three-Dimensional Printing

Optimal Selection of Basis Functions for Robust Tracking Control of Uncertain Linear Systems—With Application to Three-Dimensional Printing
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不确定线性系统鲁棒跟踪控制基函数的优化选择及其在三维打印中的应用

DOI:
10.1115/1.4051097
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发表时间:
2021
期刊:
and Control
影响因子:
--
通讯作者:
Okwudire, Chinedum E.
Okwudire, Chinedum E.
中科院分区:
--
文献类型:
--
作者:
Ramani, Keval S.;Okwudire, Chinedum E.

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由于滤波基函数(FBF)方法与文献中的其它跟踪控制方法相比具有明显的优势,因此它在跟踪线性系统,特别是非最小相位(NMP)对象方面的应用越来越受到人们的关注。FBF方法将控制输入表示为具有未知系数的基函数的线性组合。基函数通过被控对象的动态特性进行前向滤波,并且系数被选择为使得跟踪误差最小化。与其他前馈控制方法类似,FBF方法的跟踪精度在不确定性的存在下恶化。然而,与其他方法不同的是,FBF方法在基函数的选择方面具有灵活性,可以用来提高其精度。本文分析了在存在不确定性的情况下,基函数的选择对FBF跟踪精度的影响,使用FBF误差动态的提升系统表示(LSR)的Frobenius范数。在此基础上,提出了一种选择基函数以最大限度地提高鲁棒性的方法,并提出了一种应用于NMP系统时避免大的控制量的方法。从这个过程中产生的基函数被称为鲁棒基函数。实验应用于具有不确定NMP动态的桌面三维(3D)打印机,与B样条相比,使用所提出的鲁棒基函数实现了高达48%的跟踪精度提高,同时利用更少的控制工作。
There is growing interest in the use of the filtered basis functions (FBF) approach to track linear systems, especially nonminimum phase (NMP) plants, because of its distinct advantages compared to other tracking control methods in the literature. The FBF approach expresses the control input to the plant as a linear combination of basis functions with unknown coefficients. The basis functions are forward filtered through the plant dynamics, and the coefficients are selected such that tracking error is minimized. Similar to other feedforward control methods, the tracking accuracy of the FBF approach deteriorates in the presence of uncertainties. However, unlike other methods, the FBF approach presents flexibility in terms of the choice of the basis functions, which can be used to improve its accuracy. This paper analyzes the effect of the choice of the basis functions on the tracking accuracy of FBF, in the presence of uncertainties, using the Frobenius norm of the lifted system representation (LSR) of FBF's error dynamics. Based on the analysis, a methodology for optimal selection of basis functions to maximize robustness is proposed, together with an approach to avoid large control effort when it is applied to NMP systems. The basis functions resulting from this process are called robust basis functions. Applied experimentally to a desktop three-dimensional (3D) printer with uncertain NMP dynamics, up to 48% improvement in tracking accuracy is achieved using the proposed robust basis functions compared to B-splines, while utilizing much less control effort.
DOI: 10.1115/1.4044355
发表时间: 2019-11
期刊: Journal of Dynamic Systems, Measurement, and Control
影响因子: --
作者:
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发表时间: 2020-10
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影响因子: --
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