高精度数控系统中的迭代学习控制器设计
发布时间：2018-09-06 10:00
【摘要】：让机器变得智能是工控界的共同理想。在面对重复性加工任务时,如果数控机床具有“自学习”功能,主动根据之前零件的误差信息指导后续加工,就会使加工误差逐渐减小,成品率大幅提升。然而,国内现有的数控机床都只能把大批量生产当作标准单件生产的机械重复,之前的加工信息得不到利用,经过多道复杂的工序后,成品率低下。针对上述问题,本文提出为现有数控机床设计自学习控制器的想法。在众多自学习算法中,选择迭代学习控制算法。本着原理简单、便于应用、鲁棒性强的原则,最终选定控制器结构为基于干扰观测器的闭环PID(Proportional Integral Differential)型迭代学习控制器。并分为四个步骤展开工作,分别是数控系统的建模与辨识、自学习控制器结构设计、学习增益参数优化以及效果验证。在深入分析迭代算法后,发现对该算法的研究大多停留在仿真与半实物仿真阶段,于是总结出制约算法应用的两大难题。其一:算法的理论支持大多是基于开环迭代进行的,但开环系统稳定性难以保证,实际系统大多是闭环系统;其二,即使满足收敛性条件,在迭代的过程中仍存在误差先减小后增大的过冲现象。为解决第一个难题,在综合考虑迭代算法收敛精度、速度与可实现的复杂程度后,选定闭环迭代结构,并为其推导出一套学习增益参数优化方案。同时,将迭代算法只能抑制重复性扰动的限制放宽,辅之以干扰观测器抑制非重复性扰动;为解决第二个难题,提出条件启停迭代机制,实现了从学习模式到生产模式的顺利过渡。在三轴雕铣机床上进行蝴蝶轨迹跟踪效果验证时,对比基于单纯形法的最优参数自整定方法,单轴跟踪误差为33.4μm;而本文设计的自学习控制器经过十次迭代可将单轴跟踪误差降为5.705μm。在现有迭代学习的书籍文献中,大多使用通篇的公式进行原理阐述,对于初学者入门困难。本文将这些理论知识与数控机床相结合,以实际应用为前提,以最简单方法实现为基础,用通俗易懂的语言描述了控制器设计的详细过程,并将该方法写入数控系统中,实现了算法的应用价值。
[Abstract]:It is the common ideal of industrial control to make machines intelligent. In the face of repeated machining tasks, if NC machine tools have the function of "self-learning" and guide the follow-up machining according to the error information of the former parts, the machining errors will gradually decrease and the finished product rate will be greatly increased. However, the existing CNC machine tools in China can only regard mass production as a mechanical repetition of standard single-piece production, and the previous processing information is not utilized. After many complicated processes, the yield of finished products is low. In view of the above problems, this paper puts forward the idea of designing self-learning controller for existing NC machine tools. Among many self-learning algorithms, iterative learning control algorithm is chosen. Based on the principle of simple principle, easy application and strong robustness, the structure of the controller is selected as the closed-loop PID (Proportional Integral Differential) iterative learning controller based on disturbance observer. It is divided into four steps: modeling and identification of numerical control system, structure design of self-learning controller, optimization of learning gain parameters and effect verification. After deeply analyzing the iterative algorithm, it is found that the research of the algorithm is mostly in the stage of simulation and hardware-in-the-loop simulation, so two difficult problems restricting the application of the algorithm are summarized. First, the theoretical support of the algorithm is mostly based on the open-loop iteration, but the stability of the open-loop system is difficult to guarantee, and the actual system is mostly closed-loop system; second, even if the convergence condition is satisfied, In the iterative process, the error decreases first and then increases. In order to solve the first problem, after considering the convergence accuracy, speed and realizable complexity of the iterative algorithm, the closed-loop iterative structure is selected and a set of optimization scheme for learning gain parameters is derived. At the same time, the iterative algorithm can only restrain the limitation of repetitive disturbance, and the disturbance observer is used to suppress the non-repetitive disturbance. In order to solve the second problem, a conditional start / stop iterative mechanism is proposed. Realized the smooth transition from the learning mode to the production mode. When the butterfly track tracking effect is verified on a three-axis carving and milling machine tool, the optimal parameter self-tuning method based on simplex method is compared. The uniaxial tracking error is 33.4 渭 m, and the self-learning controller designed in this paper can reduce the uniaxial tracking error to 5.705 渭 m after ten iterations. In the existing iterative learning literature, most of them use the whole formula to explain the principle, so it is difficult for beginners to get started. In this paper, we combine these theories with NC machine tools, take the practical application as the premise, take the simplest method as the foundation, describe the detailed process of the controller design in a simple and understandable language, and write this method into the NC system. The application value of the algorithm is realized.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP273
本文编号：2225996
[Abstract]:It is the common ideal of industrial control to make machines intelligent. In the face of repeated machining tasks, if NC machine tools have the function of "self-learning" and guide the follow-up machining according to the error information of the former parts, the machining errors will gradually decrease and the finished product rate will be greatly increased. However, the existing CNC machine tools in China can only regard mass production as a mechanical repetition of standard single-piece production, and the previous processing information is not utilized. After many complicated processes, the yield of finished products is low. In view of the above problems, this paper puts forward the idea of designing self-learning controller for existing NC machine tools. Among many self-learning algorithms, iterative learning control algorithm is chosen. Based on the principle of simple principle, easy application and strong robustness, the structure of the controller is selected as the closed-loop PID (Proportional Integral Differential) iterative learning controller based on disturbance observer. It is divided into four steps: modeling and identification of numerical control system, structure design of self-learning controller, optimization of learning gain parameters and effect verification. After deeply analyzing the iterative algorithm, it is found that the research of the algorithm is mostly in the stage of simulation and hardware-in-the-loop simulation, so two difficult problems restricting the application of the algorithm are summarized. First, the theoretical support of the algorithm is mostly based on the open-loop iteration, but the stability of the open-loop system is difficult to guarantee, and the actual system is mostly closed-loop system; second, even if the convergence condition is satisfied, In the iterative process, the error decreases first and then increases. In order to solve the first problem, after considering the convergence accuracy, speed and realizable complexity of the iterative algorithm, the closed-loop iterative structure is selected and a set of optimization scheme for learning gain parameters is derived. At the same time, the iterative algorithm can only restrain the limitation of repetitive disturbance, and the disturbance observer is used to suppress the non-repetitive disturbance. In order to solve the second problem, a conditional start / stop iterative mechanism is proposed. Realized the smooth transition from the learning mode to the production mode. When the butterfly track tracking effect is verified on a three-axis carving and milling machine tool, the optimal parameter self-tuning method based on simplex method is compared. The uniaxial tracking error is 33.4 渭 m, and the self-learning controller designed in this paper can reduce the uniaxial tracking error to 5.705 渭 m after ten iterations. In the existing iterative learning literature, most of them use the whole formula to explain the principle, so it is difficult for beginners to get started. In this paper, we combine these theories with NC machine tools, take the practical application as the premise, take the simplest method as the foundation, describe the detailed process of the controller design in a simple and understandable language, and write this method into the NC system. The application value of the algorithm is realized.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP273
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