CAREER: Artificial Learning Control Systems for Performance Critical Applications

职业：用于性能关键应用的人工学习控制系统

• 批准号: 0238993
• 负责人: Eric Johnson
• 金额: $ 40万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0238993&HistoricalAwards=false
• 关键词: CAREER; Artificial; Learning; Control; Systems

The goal of human-like (or better) intelligent control, and systems that combine the best elements of human operators and machines to achieve a more capable whole, is held by many researchers. Controllers with intermediate levels of intelligence and adaptability (between current systems and human-like control) are significant for at least two reasons: (1) They are a stepping-stone to the more capable systems envisioned; and (2) They have important practical uses in their own right, as part of semi-intelligent systems or utilized as an element of more capable human/machine systems.This project will depart from previous important and useful artificial neural network (NN) adaptive control methods, based on a dynamic inverse or feedback linearization controller and a neural network trained online. This class of adaptive control has recently been extended by the principal investigator to include plants that are not feedback linearizable (even approximately). These methods have distinct advantages for performance critical applications (i.e., those where safety, economic pressures, or other factors imply that the probability of system failure must be minimized), because of rapid response to new uncertainties and important theoretical stability results. A new approach will be taken, which retains the advantages described above, but utilizes untapped potential of NN adaptive controllers to enable the system to learn the plant over all state and control space for which information is available, including information given a priori, recorded data, and current state. It will explore this idea of simultaneous online training and training on recorded past data within other adaptive control frameworks. The development of these approaches is hoped to lead to many important new applications of performance critical controllers, applications that generally required a human "touch" to achieve acceptable performance (and also require stability and robustness guarantees), examples include: aspects of factory automation (those where uncertainty is not negligible), ground vehicle control, construction equipment, integrated avionics, spacecraft docking, and flight control. This predicted impact is based on providing an important element of what humans can do as controllers: become highly proficient at controlling a plant over a large segment of state and control space, yet also able to respond to a sudden change in plant dynamics.

许多研究人员的目标是实现类人（或更好）的智能控制，以及将人类操作员和机器的最佳元素结合起来以实现更有能力的整体的系统。具有中等水平智能和适应性（介于当前系统和类人控制之间）的控制器具有重要意义，至少有两个原因：(1)它们是通向更有能力的系统的垫脚石；(2)它们本身具有重要的实际用途，作为半智能系统的一部分或作为更有能力的人/机器系统的组成部分。该项目将不同于以往重要和有用的人工神经网络（NN）自适应控制方法，基于动态逆或反馈线性化控制器和在线训练的神经网络。这类自适应控制最近被首席研究员扩展到包括非反馈线性化（甚至近似）的植物。由于对新的不确定性和重要的理论稳定性结果的快速响应，这些方法对于性能关键应用（即那些安全、经济压力或其他因素意味着必须最小化系统故障概率的应用）具有明显的优势。将采用一种新的方法，它保留了上述优点，但利用了神经网络自适应控制器未开发的潜力，使系统能够在所有可用信息的状态和控制空间中学习植物，包括先验信息、记录数据和当前状态。它将探讨在其他自适应控制框架内同时进行在线培训和对记录的过去数据进行培训的想法。这些方法的发展有望导致许多重要的性能关键控制器的新应用，这些应用通常需要人类“触摸”来实现可接受的性能（并且还需要稳定性和鲁棒性保证），例如：工厂自动化方面（不确定性不可忽略的方面），地面车辆控制，建筑设备，集成航空电子设备，航天器对接和飞行控制。这种预测的影响是基于提供人类作为控制者所能做的一个重要元素：在大范围的状态和控制空间中高度精通控制植物，同时也能够对植物动态的突然变化做出反应。