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: 8912397
• 负责人: Kumpati Narendra
• 金额: $ 35.28万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-09-15 至 1993-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8912397&HistoricalAwards=false
• 关键词: Adaptive; Identification; Control; Dynamical; Systems

The focus of this research is on the adaptive identification and control of complex nonlinear dynamical systems using neural networks. From the theoretical point of view the main aim is to generate stable adaptive laws for the adjustment of the parameters of the system. From the practical viewpoint the aim is to generate control methods which yield high performance in complex environments. The nonlinear nature of the neural networks makes stability analysis of the overall system and extremely difficult task. Hence, it is essential to confine our attention to a class of systems which have been shown at least empirically to perform successfully. The research will consequently be carried out in the following four stages: (i) determination of suitable dynamic structures for identification and control models which are applicable to a wide class of nonlinear plants, (ii) development of dynamic back propagation methods for adjusting controller parameters, (iii) use of such methods to carry out extensive computer simulations on the identification and control of complex systems (iv) stability analysis of the class of systems which prove efficient in (iii).

本文研究的重点是自适应辨识和 用神经网络控制复杂的非线性动态系统。 从理论的角度来看，主要目的是产生稳定的 用于调整系统参数的自适应律。 从实用的角度来看，目的是产生控制方法 其在复杂环境中产生高性能。 神经网络的非线性性质使得稳定性分析 这是一个非常艰巨的任务。 因此，它是 必须把我们的注意力集中在一类系统上， 至少从经验上看是成功的。 研究 因此，将分以下四个阶段进行： (i)确定用于识别的合适动态结构 和控制模型，适用于广泛的一类非线性 植物， (ii)动态反向传播方法的发展， 控制器参数， (iii)使用这种方法进行广泛的计算机模拟 复杂系统的识别与控制 (iv)证明有效的一类系统的稳定性分析 （iii）。