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Adaptive neural network control with inverse model: Application to the control of turning process.

机译：逆模型的自适应神经网络控制：在车削过程控制中的应用。

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摘要

This dissertation presents adaptive on-line neural network control schemes for unknown dynamical processes and eventually for the control of feed force in turning process. The research consists of four tasks: Development of a machining process model, development of a neural network control structure and adaptation schemes, machining experiments to collect data for building a simulation model, and application of proposed control schemes to turning process.; By studying the process models in the literature, a turning process model on a CNC lathe is developed. The proposed model is nonlinear and sampling time is independent of spindle rpm. The characteristics of noise and the parameters of cutting dynamics are determined by the experimental data. To collect cutting force data, a SAE 6150 steel is turned with 25 sets of machining conditions on a 20HP LeBlond 1610 lathe.; The proposed adaptive inverse model (AIM) control scheme has similar structure as the model reference adaptive controller (MRAC). In AIM control, neural networks are used as the system identifier and the controller. The system identifier and the controller are first trained off-line to be a forward model and an inverse model of the process respectively. The off-line training is simultaneously done for the same set of data with different arrangements. During on-line adaptation, the system identifier is iteratively used to convert tracking errors into control errors and to calculate the current and past tracking errors after changing the weights of the controller. To be robust to noises, the controller not only considers the current error but also looks back to the past to minimize the "would-have-been" tracking errors since the change in weights of the controller would have generated different control inputs in the past, and, in turn, different tracking error.; Two implementations of AIM controllers, using feedforward networks and using cerebellar model articulation controllers (CMACs), are applied to control the feed force in turning along with an MRAC for comparison. Simulation results show that AIM controllers, which are designed without structural information about the dynamics of the process, perform comparably well with the MRAC. Especially for the process with severe noise, AIM controller with feedforward networks outperformed the others.; This research also provides a collective view on neural networks in on-line process control. Real-time back-propagation (RTB) and back-propagation through time (BTT) learning algorithms are presented for multilayer neural networks with internal time-delays. Convergent CMAC learning algorithms for on-line process identification are developed with proofs.; This research is unique in that we can design a control system without knowing the dynamics of the process. The proposed control system can outperform other control techniques since it is based on neural networks, which are noise-tolerant and can accurately represent nonlinearity. Application of neural network-based control schemes to the machining process is also notable. The parallel structure of neural networks can make integration easier with other concepts such as tool-wear monitoring, intelligent control, and supervisory control.
机译：本文针对未知的动力学过程提出了自适应的在线神经网络控制方案，并最终为车削过程中的进给力控制提供了方法。该研究包括四个任务：加工过程模型的开发，神经网络控制结构和适应方案的开发，加工实验以收集用于构建仿真模型的数据以及拟议的控制方案在车削过程中的应用。通过研究文献中的过程模型，开发了数控车床上的车削过程模型。提出的模型是非线性的，采样时间与主轴转速无关。噪声特性和切削动力学参数由实验数据确定。为了收集切削力数据，在25HP加工条件下，在20HP LeBlond 1610车床上车削SAE 6150钢。所提出的自适应逆模型（AIM）控制方案具有与模型参考自适应控制器（MRAC）类似的结构。在AIM控制中，神经网络用作系统标识符和控制器。首先对系统标识符和控制器进行脱机训练，使其分别成为过程的正向模型和逆向模型。离线培训是针对具有不同安排的同一组数据同时进行的。在在线适配期间，系统标识符被迭代地用于将跟踪误差转换为控制误差，并在改变控制器的权重之后计算当前和过去的跟踪误差。为了对噪声具有鲁棒性，控制器不仅考虑了当前误差，还回顾了过去，以最大程度地减少“将要出现”的跟踪误差，因为控制器的重量变化过去可能会产生不同的控制输入，进而产生不同的跟踪错误。使用前馈网络和使用小脑模型关节运动控制器（CMAC）的AIM控制器的两种实现方式可用于控制进给力以及MRAC进行比较，以进行比较。仿真结果表明，设计时没有关于过程动力学的结构信息的AIM控制器与MRAC的性能相当。特别是对于噪声严重的过程，带有前馈网络的AIM控制器的性能优于其他控制器。这项研究还提供了在线过程控制中神经网络的集体观点。针对具有内部时间延迟的多层神经网络，提出了实时反向传播（RTB）和时间反向传播（BTT）学习算法。利用证明开发了用于在线过程识别的融合CMAC学习算法。这项研究的独特之处在于，我们可以在不了解过程动态的情况下设计控制系统。所提出的控制系统由于其基于神经网络的性能优于其他控制技术，因此该神经网络具有噪声容限并且可以准确地表示非线性。基于神经网络的控制方案在加工过程中的应用也很明显。神经网络的并行结构可以使与工具磨损监控，智能控制和监督控制等其他概念的集成更加容易。

著录项

  • 作者

    Sheen, Dongmok.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Industrial.; Engineering Mechanical.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 223 p.
  • 总页数 223
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;机械、仪表工业;人工智能理论;
  • 关键词

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