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Analytical and experimental study of nonlinear structural control using neural networks.

机译:基于神经网络的非线性结构控制的分析和实验研究。

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摘要

A comprehensive analytical and experimental study of actively controlled nonlinear structures using the learning capabilities of the neural networks is presented. The method utilizes the neural networks learning capability of the control tasks and referred to as neuro-control method. The neurocontrollers are developed and trained by the use of independent neural network models called the emulator neural networks. The emulator neural networks provide a training path for the neurocontrollers as well as identify non-parametric models of the controlled system. This study is developed and conducted in two consecutive phases. The first phase comprised the analytical study of nonlinear structural control. In this phase, two neurocontrollers with their associated emulator neural networks are developed, trained, assessed and tested in numerical simulation of a three story steel frame model. First neurocontroller is trained when the response of the structure remained within the linearly elastic range, and has been called the linearly-trained neurocontroller ( LTC). The second neurocontroller is trained from the nonlinear response of the structure, and has been called the nonlinearly-trained neurocontroller (NTC). The emulators effectiveness in model reprehension is presented and discussed. Similarly, both neurocontrollers, effectiveness and robustness are evaluated and presented.; The second phase of this study includes a comprehensive experimental verification of the structural neuro-control method. These experiments are carried out on the earthquake simulator at the University of Illinois at Urbana-Champaign. Two linearly-trained neurocontrollers, referred to as U3A and UA, with different architecture, feedback and sampling rates are developed, experimented and evaluated. The training procedure for the neurocontrollers were achieved by the aid of multiple emulator neural networks in parallel-series fashion. These emulator neural networks vary in their prediction capabilities, sampling rates and time delays. Together, they constitute the neurocontroller training source. The stability and robustness of the neurocontrollers are demonstrated analytically and experimentally.; Finally, a linear quadratic Gaussian optimal regulator ( LQG) is designed from an experimentally identified model. Then, the optimal controller is assessed and compared to the neurocontroller results. Additionally, the stability and robustness of the controller is illustrated and outlined. It is shown experimentally that the neurocontrollers performance is superior to the optimal controller performance.
机译:利用神经网络的学习能力,对主动控制的非线性结构进行了全面的分析和实验研究。该方法利用控制任务的神经网络学习能力,称为神经控制方法。通过使用称为仿真器神经网络的独立神经网络模型来开发和训练神经控制器。仿真器神经网络为神经控制器提供了训练路径,并识别了受控系统的非参数模型。这项研究是在两个连续的阶段中进行的。第一阶段包括非线性结构控制的分析研究。在此阶段,将在三层钢框架模型的数值模拟中开发,训练,评估和测试两个神经控制器及其相关的仿真器神经网络。当结构的响应保持在线性弹性范围内时,将训练第一个神经控制器,该神经控制器被称为线性训练神经控制器(LTC)。第二个神经控制器是从结构的非线性响应中训练出来的,因此被称为非线性训练神经控制器(NTC)。提出并讨论了仿真器在模型解析中的有效性。同样,评估并介绍了神经控制器,有效性和鲁棒性。这项研究的第二阶段包括对结构神经控制方法的全面实验验证。这些实验是在伊利诺伊大学香槟分校的地震模拟器上进行的。开发,试验和评估了两个线性训练的神经控制器,分别称为U3A和UA,它们具有不同的体系结构,反馈和采样率。借助多个仿真器神经网络以并行串联方式实现了神经控制器的训练过程。这些仿真器神经网络的预测能力,采样率和时间延迟都不同。它们共同构成了神经控制器的训练源。分析和实验证明了神经控制器的稳定性和鲁棒性。最后,根据实验确定的模型设计了线性二次高斯最优调节器(LQG)。然后,评估最佳控制器并将其与神经控制器结果进行比较。另外,示出并概述了控制器的稳定性和鲁棒性。实验表明,神经控制器的性能优于最佳控制器的性能。

著录项

  • 作者

    Bani-Hani, Khaldoon A.;

  • 作者单位

    University of Illinois at Urbana-Champaign.;

  • 授予单位 University of Illinois at Urbana-Champaign.;
  • 学科 Engineering Civil.; Computer Science.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1998
  • 页码 421 p.
  • 总页数 421
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;自动化技术、计算机技术;人工智能理论;
  • 关键词

  • 入库时间 2022-08-17 11:48:37

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