| Author Name | Affiliation | | Jiangang Li | School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055,Guangdong,China | | Xiaodong Wang | KEJIE Machinery Automation Co., Ltd., Jiangmen 529030, Guangdong,China | | Miaosen Chen | School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055,Guangdong,China | | Yiming Ma | School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055,Guangdong,China |
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| Abstract: |
| The repetitive processing and large quantity of single product represented by 3C products are urgently needed. However, for current processing operations, previous processing data have not been used in the optimization of control input. In order to utilize previous processing data to facilitate the next process and avoid adverse effects caused by repetitive disturbance and noise, the idea of iterative learning was introduced to improve the accuracy of machining. On the control level, since it is difficult to obtain high accuracy by traditional feedback control when faced with complex trajectories, an open-loop iterative learning controller and a position loop feedback controller were introduced, which worked fast with good convergence effects. Aiming at reducing the influence of accidental error, step type iterative learning was put forward. The iteration mechanism was stopped when the accuracy converged to the allowable range so as to reduce computational complexity, store the current iterative part of the control input, and make constant value compensation. However, in simulation and experiment, it was found that after superposition of the iterative learning controller, the phenomenon of partial divergence of the system tracking error occurred. Therefore, the speed and acceleration characteristics of input trajectories in time domain and frequency domain were analyzed. High-frequency noise was introduced in frequency domain, which was found to be the cause of the abovementioned phenomenon, and high-frequency components were filtered to solve the problem. To further improve the accuracy of convergence and avoid filtering effective high-frequency information in some area, a switchable filter based on the analysis of the frequency characteristics of input trajectory was proposed. Through SIMULINK simulation and dSPACE experimental verification, it was proved that the iterative learning controller of modifying controlled quantity and filter based iterative learning control method are effective. |
| Key words: iterative learning control ladder iterative learning switchable iterative mechanism filter design switchable filter design |
| DOI：10.11916/j.issn.1005-9113.2019024 |
| Clc Number:TG659 |
| Fund: |
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| Descriptions in Chinese: |
| 数控机床加工中的迭代学习控制器设计 李建刚1，王小东2，陈淼森1，马一鸣1 （1. 哈尔滨工业大学（深圳）机械工程与自动化学院，深圳 518055；2.广东科杰机械自动化有限公司，广东 江门 529030） 创新点说明：1） 针对迭代学习控制器设计中的误差发散现象，给予了理论性的分析，并提出了滤波型迭代学习机制来解决问题； 2）提出了可切换型滤波器迭代学习机制，用于进一步提高数控加工精度。 研究目的： 在目前工厂的大批量加工模式下，通过迭代学习控制，使得数控机床的加工可以从以前的加工信息中进行学习，从而改善后续加工情况，不断提高加工精度和加工产品的质量。 研究方法： 1）所使用的主要设备和仪器：TIMAX三轴机床以及配套的松下A4系列驱动器，dSPACE控制卡。 2）通过对比不同的迭代学习结构优缺点，采用合适的迭代学习结构进行控制器设计。并进行实验验证。 3）通过实验验证所设计的控制器效果，分析实验结果后发现误差会随着迭代的进行而发散，并称这种现象为不好暂态，通过理论分析说明该现象出现的原因，并提出开关型迭代学习机制和滤波型迭代学习机制解决该问题。 4）经过进一步研究发现，单独使用滤波器型迭代学习机制，可能会使较为复杂的信号丢失掉一部分高频信息，这样会导致误差收敛精度难以进一步提高。针对此现象，对输入信号和误差信号进行时频分析，从而确定有效高频动态存在的时间区段，进行可切换滤波器迭代学习机制的设计，最终通过实验验证该方法可进一步提高收敛性能。 研究结果： 1）采用蝴蝶轨迹的单轴信息进行实验，验证所设计的迭代学习控制器的效果，在一定程度上可以不断降低误差，但是迭代到一定次数会出现不好暂态的现象。 2）通过实验验证滤波型迭代学习控制器的效果，在实验结果中发现，不好暂态的现象消失了，误差可以稳定收敛到一定程度。 3）通过对输入信号和误差信号进行时频分析，选用合适的可切换滤波器，并进行实验验证，误差的收敛程度得到了进一步的提升。 结论： 1） 引入迭代学习控制后，相比传统的速度前馈的方法，误差有很大程度的降低。 2）采用滤波型迭代学习机制，可有效解决不好暂态这种现象，误差可以稳定收敛到一定程度。 3）采用可切换型滤波器迭代学习方法可使收敛精度进一步提高，最大误差保持在0.01mm以内，相比普通的滤波型迭代学习机制，收敛精度有进一步提高。 关键词：迭代学习控制；阶梯迭代学习；可切换迭代机制；滤波器设计；可切换滤波器设计 |
