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sa    2017, Vol. 3 Issue (5) : 641-647
Research |
1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710054, China
2. Collaborative Innovation Center of High-End Manufacturing Equipment, Xi’an Jiaotong University, Xi’an 710054, China
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摘要 滚珠丝杠进给驱动系统由于其细长的螺旋主轴和部件之间连接部分的灵活性,具有变化的高阶动态特性,并且当采用直线光栅尺进行直接位置测量时,它还具有非同位控制的明显特征。长期以来,动态特性和非同位状况一直是运动和振动控制困难的根源,它们还会降低轴的运动精度。在本研究中,考虑到灵活性及其变化,我们建立了基于频率的子结构动态模型,并充分研究了动态特性对位置变化的依赖,开发出由模态特征修正器(MCM)和智能自适应调整算法(ATA)组成的控制策略。MCM由峰值滤波器和陷波滤波器组成,从而将设备动态变为虚拟同位系统,且可以避免控制溢出。使用人工神经网络(ANN)作为平滑参数插值器,可以实时更新滤波器的参数,以便应对进给驱动系统的动态变化。我们已经在实际的进给驱动系统中验证了所提出策略的有效性和鲁棒性。
关键词 滚珠丝杠进给驱动系统变化高阶动态非同位控制模态特征修正器智能自适应调整算法    

The ball-screw feed drive has varying high-order dynamic characteristics due to flexibilities of the slender screw spindle and joints between components, and an obvious feature of non-collocated control when a direct position measurement using a linear scale is employed. The dynamic characteristics and non-collocated situation have long been the source of difficulties in motion and vibration control, and deteriorate the achieved accuracy of the axis motion. In this study, a dynamic model using a frequency-based substructure approach is established, considering the flexibilities and their variation. The position-dependent variation of the dynamic characteristics is then fully investigated. A corresponding control strategy, which is composed of a modal characteristic modifier (MCM) and an intelligent adaptive tuning algorithm (ATA), is then developed. The MCM utilizes a combination of peak filters and notch filters, thereby shaping the plant dynamics into a virtual collocated system and avoiding control spillover. An ATA using an artificial neural network (ANN) as a smooth parameter interpolator updates the parameters of the filters in real time in order to cope with the feed drive’s dynamic variation. Numerical verification of the effectiveness and robustness of the proposed strategy is shown for a real feed drive.

Keywords Ball-screw feed drives      Varying high-order dynamics      Non-collocated control      Modal characteristic modifier      Intelligent adaptive tuning algorithm     
最新录用日期:    在线预览日期:    发布日期: 2017-11-08
Hui Liu
Jun Zhang
Wanhua Zhao
Hui Liu,Jun Zhang,Wanhua Zhao. An Intelligent Non-Collocated Control Strategy for Ball-Screw Feed Drives with Dynamic Variations[J]. Engineering, 2017, 3(5): 641-647.
网址:     OR
Fig.1  Scheme of a 3M2S system. m1, m2, m3 are the three masses; k1 and k2 are the two springs; c1, c2, and cm2 are the dampings; x1, x2, and x3 are the displacements; and F1 is the force applied on m1.
Fig.2  FRFs of the 3M2S system. (a) h11; (b) h21; (c) h31.
Fig.3  Operating deflection shapes of the (a) rigid motion and (b) two resonant modes. θ1, θ2, and θ3 are the receptances of the three coordinates.
Fig.4  PD collocated control and its passive mechanical equivalence. kp: proportional gain; kd: derivative gain; r: given displacement.
Fig.5  Nyquist plots of collocated and non-collocated situations for 3M2S. v1/F1 and v2/F1 are the velocity receptances.
Fig.6  Collocated and non-collocated control in ball-screw feed drives.
Fig.7  (a) A general model considering the non-collocated situation and (b) its symbolic representation.
Fig.8  Three subsystems of a ball-screw feed drive.
Fig.9  Schematic model of the tri-subsystem receptance coupling via the screw-nut. Block “× d” stands for multiplication by the diameter of the ball screw.
Fig.10  Varying dynamics shown with families of magnitude plots (Ytable = 100–700 mm). (a) T1 in and θ1 out; (b) F3 in and θ1 out; (c) T1 in and x2 out; (d) F3 in and x2 out; (e) T1 in and x3 out; (f) F3 in and x3 out.
Fig.11  Varying dynamics h21 shown with cloud images (Ytable = 100–700 mm).
Fig.12  Resonant phase variation with table position (h21).
Fig.13  The proposed intelligent non-collocated control strategy. P: proportional; PI: proportional-integral.
Fig.14  Modifying effects of the proposed peak filter, where w/ variation refers to with variation.
Fig.15  The structure of the proposed ATA based on an ANN.
Mode Natural frequency (Hz) Damping ratio Phase(º)
1 49 0.120 –135
2 95 0.170 –155
3 130 0.090 –260
4 1030 0.003 –198
5 1529 0.010 –31
Tab.1  Modal characteristics identified for the test plant (Ytable= 115 mm).
Fig.16  Tracking responses and errors with PID control (Ytable = 115 mm), where w/ FFC refers to with feed-forward control and w/o FFC refers to without feed-forward control.
Fig.17  Tracking responses and errors with the proposed MCM control (Ytable = 115 mm).
Fig.18  An RMS errorcomparison.
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