SBIR Phase II: Neural Networks, Youla Parameterization and Reconfigurable Control Systems

SBIR 第二阶段：神经网络、Youla 参数化和可重构控制系统

• 批准号: 9321264
• 负责人: Richard Saeks
• 金额: $ 31.98万
• 依托单位: Accurate Automation Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-10-01 至 1997-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9321264&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Neural; Networks

9321264 Saeks This project will develop a hybrid frequency domain/neurocontrol algorithm by using neural network techniques to adjust the "Youla design parameter." This yields a controller which is guaranteed to be stable and can implement any of and /or distrubance rejection, pole placement, robust tracking and/or disturbance rejection, simultaneous design, initial value design, etc. The adaptivity and robustness intrinsic to neurocontrol is integerated into the frequency domain controller. Powerful (ontogenic) neural network training techniques may be brought to bare on the control system design problem allowing one to design a controller without a prior restrictions on its order. A reconfigurable control architecture is developed where the controller is trained to deal with multiple scenarios; alternative trajectories for a flight controller, varying loads on a robot arm, alternative current waveshapes in a power supply, etc; generaliziang to new scencarios byond the training set where necessary. The approach is directly applicable to linear multivariate systems and extendable, via the vairous generalizations of the Youla parameterization to the nonlinear case. *** v s t disturbanceintegratedetc.generalizingscenariosbeyondextendiblevariousB C D E F G I N O P Q R S T U V W X Y Z \ ^ _ ` a b g h S j k l m n r s t u v w x y z { } ~ 9321264 Saeks This project will develop a hybrid frequency domain/neurocontrol algorithm by using neural net work techniques to . = H R V b k q { ) . $ ( F / . $ . 1 Times Symbol " Helvetica 5 Courier New E O Y ` = H N R 4 V C b U k q { , " h j2 % 9 E ( S R:\WW20USER\ABSTRACT.DOT abstract Alicia E. Harris Alicia E. Harris

9321264 Saeks该项目将开发一种混合频域/神经控制算法，通过使用神经网络技术来调整“Youla设计参数”。这就产生了一个控制器，它保证是稳定的，可以实现任何和/或扰动抑制，极点配置，鲁棒跟踪和/或扰动抑制，同步设计，初始值设计等神经控制固有的自适应性和鲁棒性集成到频域控制器。 强大的（个体发育）神经网络训练技术可能会带来裸露的控制系统设计问题，允许一个设计控制器没有事先限制其顺序。 开发了一种可重构控制体系结构，其中控制器被训练以处理多个场景;飞行控制器的替代轨迹，机器人手臂上的变化负载，电源中的替代电流波形等;在必要时通过训练集推广到新的场景。 该方法可直接应用于线性多元系统，并可通过将Youla参数化推广到非线性情形而得到推广。 *** v s t disturbance integrated detc. generalizing beyond extendiblevarious B C D E F G 我 公司简介企业文化荣誉资质 \ ^ _ ` a B g h S j k l m n [001 pdf 1st-31files] r s t u v w y z { } ~ 9321264 Saeks本项目将通过使用神经网络技术开发一种混合频域/神经控制算法， . = H R V B K Q { ) . $ ( F / . $ . 1 倍 Symbol“Helvetica 5新 E O Y ` = H N R 4 V C B U K Q { , " H j2% 9 E ( S R：\WW20USER\ABSTRACT.DOT摘要 艾丽西娅·E哈里斯·艾丽西娅·E.哈里斯