Nonlinear Network Structures for Dynamic System Control

动态系统控制的非线性网络结构

• 批准号: 0140490
• 负责人: Frank Lewis
• 金额: $ 20万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-15 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0140490&HistoricalAwards=false
• 关键词: Nonlinear; Network; Structures; Dynamic; System

AbstractThe complexity of modem day aerospace, industrial, DoD, civil infrastructure, and vehicle systems is increasing, and performance requirements are becoming more stringent in terms of both accuracy and speed of response. Such systems are characterized by complex dynamics having nonlinearities, unmodeled dynamics, flexibility effects, varying parameters, unknown friction, high amplitude disturbances, and actuators with deadzones, backlash, and saturation. The control problems associated with such complex systems are not easy, as they do not satisfy most of the assumptions made in the controls literature. Therefore, most existing control algorithms do not work well. Optimal nonlinear control systems hold out the hope of successfully confronting these problems but are expressed in terms of solutions to the Hamilton-Jacobi-Bellman (HJB) equation. However, HJB is not analytically solvable for practical systems, and the dynamic programming solution for discrete-time systems suffers from NP-complexity problems ('the curse of dimensionality'). Approximate solution techniques for the HJB equation have been explored and show great promise in reducing NP-complexity issues. However, such HJB approximate techniques must be tied to real-time on-line feedback control techniques that simultaneously stabilize the system while adapting to approximate the HJB solution.Recent developments show that nonlinear network structures, both neural network (NN) and fuzzy logic (FL) systems, hold out the hope of providing approximate solutions to the HJB equation and other nonlinear design equations. Structured nonlinear networks hold out the hope for confronting problems of NP-complexity in complex systems control. The structure inherent in FL systems holds out the hope of designing new NN architectures of increased structure. Approximate HJB solution also holds out the hope of bridging the gap between high-level computer science architectures and servo-level feedback control. This research has three goals: (1) Nearly Optimal HJB Control Using Neural Networks; (2) 0N0ovel 00002High-Level Nonlinear Network Control Architectures; (3) and UTA/High School Teams and Courseware for Nonlinear Network Control.

摘要现代航空航天、工业、国防部、民用基础设施和车辆系统的复杂性不断增加，并且在精度和响应速度方面的性能要求也变得更加严格。此类系统的特点是具有非线性的复杂动力学、未建模的动力学、柔性效应、变化的参数、未知的摩擦、高振幅扰动以及具有死区、间隙和饱和的执行器。与此类复杂系统相关的控制问题并不容易，因为它们不满足控制文献中所做的大多数假设。因此，大多数现有的控制算法都不能很好地工作。最优非线性控制系统有望成功解决这些问题，但以 Hamilton-Jacobi-Bellman (HJB) 方程的解表示。然而，HJB 无法对实际系统进行分析求解，并且离散时间系统的动态规划解决方案会遇到 NP 复杂性问题（“维数灾难”）。 HJB 方程的近似求解技术已经被探索出来，并在减少 NP 复杂性问题方面显示出巨大的前景。然而，这种 HJB 近似技术必须与实时在线反馈控制技术相结合，该技术可以在稳定系统的同时适应近似 HJB 解。最近的发展表明，非线性网络结构，包括神经网络 (NN) 和模糊逻辑 (FL) 系统，有望为 HJB 方程和其他非线性设计方程提供近似解。结构化非线性网络为解决复杂系统控制中的NP复杂性问题带来了希望。 FL 系统固有的结构为设计增加结构的新神经网络架构带来了希望。 近似 HJB 解决方案也有望弥合高级计算机科学架构和伺服级反馈控制之间的差距。这项研究有三个目标：（1）使用神经网络实现近乎最优的 HJB 控制； (2) 0N0ovel 00002高级非线性网络控制架构； (3) 以及 UTA/高中团队和非线性网络控制课件。