Adaptive Identification and Control of Dynamical Systems Using Neural Networks

使用神经网络的动态系统的自适应识别和控制

• 批准号: 9203928
• 负责人: Kumpati Narendra
• 金额: $ 41.31万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-06-01 至 1995-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9203928&HistoricalAwards=false
• 关键词: Adaptive; Identification; Control; Dynamical; Systems

The work carried out at Yale during the last three years has resulted in a number of neural network models for the identification and control of nonlinear dynamical systems. It has also raised numerous theoretical and practical questions concerning the prior information that will be needed to control the plant adaptively, as well as to assure its robustness in more complex environments. To address these question, it is proposed to investigate the following four related areas: (i) Extension of the methods developed to the control of nonlinear systems in the presence of bounded disturbances, and time- varying parameters; development of multilevel controllers for fault tolerant systems. (ii) New classes of plants for which stable adaptive algorithms can be generated. (iii) Properties of multilayer neural networks and radial basis function networks which are used as components in complex dynamical systems and their impact on overall performance. (iv) Theoretical basis for all the investigations carried out using results from nonlinear control theory.

在耶鲁大学进行的工作 在过去的三年里， 在许多神经网络中 识别模型， 非线性动态控制 系统. 也引发 许多理论和实践 关于先前的问题 将需要的信息， 自适应地控制工厂，如 并确保其稳健性， 更复杂的环境。 到 解决这些问题， 建议调查 以下四个相关领域： (i)方法的扩展 发展到控制 非线性系统 有界扰动，以及时间- 变化参数;发展 多级故障控制器 宽容的制度 (ii)植物的新分类 稳定的自适应算法 可以生成。 (iii)多层膜的性质 神经网络与径向基 功能网络，用作 复动力学分量 系统及其对整体 性能 (iv)所有这些的理论基础 调查使用 非线性控制结果 理论