High-Acceleration Precision Point-to-Point Motion Control With Look-Ahead Properties

High-Acceleration Precision Point-to-Point Motion Control With Look-Ahead Properties
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具有前瞻特性的高加速度精密点对点运动控制

DOI:
10.1109/tie.2010.2098363
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发表时间:
2011-09
影响因子:
7.7
通讯作者:
吴建华
吴建华
中科院分区:
计算机科学1区
文献类型:
--
作者:
吴建华

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本文研究了两种控制算法对高性能点到点运动的影响。这里的重点是克服高加速度表在存在显着的外部干扰和激励振动的精确定位的挑战:A型迭代学习控制(ILC)(A-ILC)算法的重复运动和前瞻有限脉冲响应(FIR)滤波器加滑模控制(SMC)的非重复运动。在频域中给出了A-ILC的无模型收敛条件和最快收敛的参数方程。然后根据迭代学习的结果确定FIR系数,并对系数进行修正以消除摩擦效应。实验研究表明,这两种算法表现良好,FIR-SMC算法是强大的,在各种实验场景,其中包括高加速度(73.7米/秒2或约7.5克),模型参数,和干扰偏差的位置,速度和加速度在ILC(因此,FIR)的训练。
This paper investigates the effects of two control algorithms on high-performance point-to-point motions. The emphasis here is to overcome challenges in precision positioning of high-acceleration tables in the presence of significant external disturbances and exited vibration: an A-type of iterative learning control (ILC) (A-ILC) algorithm for repetitive motions and a look-ahead finite impulse response (FIR) filter plus sliding-mode control (SMC) for nonrepetitive motions. The model-free convergence condition and the fastest converging parameter equation for A-ILC are given in the frequency domain. Then, the FIR coefficients are decided through the ILC results and modified to eliminate the friction effect. Experimental studies demonstrate that both the algorithms perform well and the FIR-SMC algorithm is robust in various experimental scenarios which include high acceleration (of 73.7 m/s2 or about 7.5 g), model parameters, and disturbance deviations from the position, velocity, and acceleration at which the ILC (and, hence, FIR) is trained.
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