Development of Adaptive Learning Algorithms for High-Performance Control of Robotic Systems

机器人系统高性能控制的自适应学习算法的开发

• 批准号: 9810398
• 负责人: Degang Chen
• 金额: $ 15万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9810398&HistoricalAwards=false
• 关键词: Development; Adaptive; Learning; Algorithms; Performance

9810398ChenThe past few decades have witnessed tremendous growth in the deployment of robotic systems to improve production rate and product quality. The vast majority of these industrial robots are used for simple, repetitive tasks. Traditional robot controllers do not take advantage of the repetitive nature and the same performance error will persist in all repetitive executions. Consequently, over eighty percent of today's robots are restricted to tasks requiring relatively low precision. It is predicted, however, that future growth will be in high performance robotic systems for tasks such as electronic assembly. Learning Control, being able to learn from past experience to eliminate errors in future trials, has great potential to emerge as the controller of choice for future robotic systems. The bottleneck hindering this technology is its inability to handle non-minimum phase dynamics that arise in high-speed operation and in robotic systems with flexible components.The main objective of this project is to develop a new control methodology for the precise and high-speed execution of repetitive tasks for robotic systems with light-weight flexible components. The technical approach is inversion-based adaptive learning control that learns both the optimal control input and the system model from past execution data. This approach will resolve the above mentioned bottleneck by incorporating into the learning algorithms techniques from stable inversion, a theory recently developed for handling non-minimum phase systems. The project will also speed up the application of the new technology to industrial robotic systems by validating its effectiveness through simulation and experimental testing and by resolving technical issues arising in its practical implementation.Utilizing the PI's eight years of experience in robotic control and his expertise in stable inversion, the three year project will deliver the following: 1) a family of adaptive learning control algorithms designed to work for both minimum and non-minimum phase systems; 2) a set of nonlinear tools that enable the design and implementation of adaptive learning controllers for nonlinear systems that arise in light-weight robotic systems; 3) an experimental testbed with light-weight flexible components (a 3D crane reconfigurable for various testing purposes); 4) extensive simulation and experimental testing, validating the new control technology and demonstrating its high-performance in practical implementation.In addition to expanding the knowledge base in systems and control, the successful delivery of this control technology will have a significant impact on industries involved with robotic systems. The deployment of the new technology will bring about improved product quality since it empowers the robot to learn and eliminate repetitive errors. Production rate will be higher since the new controller can overcome difficulties caused by structural flexibility in high speed operation. Various cranes, currently in widespread use and manually controlled by skilled operators, can be automated using the new controller to achieve high performance at high speed, effectively converting them into programmable robots. The technology also promotes design and deployment of lighter-weight robotics systems, which in turn leads to faster speed, less energy consumption, less material waste, and other environmental and societal benefits. ***

9810398陈在过去的几十年里，机器人系统的部署有了巨大的增长，以提高生产率和产品质量。 这些工业机器人中的绝大多数用于简单的重复性任务。 传统的机器人控制器没有利用重复的性质，并且相同的性能误差将在所有重复执行中持续存在。 因此，今天超过80%的机器人仅限于精度要求相对较低的任务。 然而，据预测，未来的增长将是在高性能机器人系统的任务，如电子装配。 学习控制，能够从过去的经验中学习，以消除未来试验中的错误，具有很大的潜力，成为未来机器人系统的首选控制器。 阻碍这项技术的瓶颈是它无法处理非最小相位动态，出现在高速操作和机器人系统中的柔性组件。本项目的主要目标是开发一种新的控制方法，用于精确和高速执行重复性任务的机器人系统与重量轻的柔性组件。 技术方法是基于逆的自适应学习控制，其从过去的执行数据学习最优控制输入和系统模型。 这种方法将解决上述瓶颈，通过纳入学习算法的技术，从稳定的逆，最近开发的理论处理非最小相位系统。 该项目还将通过模拟和实验测试验证新技术的有效性，并解决实际实施中出现的技术问题，加快新技术在工业机器人系统中的应用。利用PI在机器人控制方面的八年经验和他在稳定反演方面的专业知识，该三年项目将提供以下内容：1）设计用于最小和非最小相位系统的自适应学习控制算法族; 2）一组非线性工具，其使得能够设计和实现用于轻型机器人系统中出现的非线性系统的自适应学习控制器; 3）具有轻质柔性部件的实验测试台（可重新配置用于各种测试目的的3D起重机）; 4）广泛的模拟和实验测试，验证新的控制技术，并证明其在实际实施中的高性能。除了扩大系统和控制的知识基础，这种控制技术的成功交付将对涉及机器人系统的行业产生重大影响。 新技术的部署将提高产品质量，因为它使机器人能够学习和消除重复性错误。 由于新的控制器可以克服高速操作中结构灵活性所带来的困难，生产率将更高。 目前广泛使用并由熟练操作员手动控制的各种起重机可以使用新的控制器实现自动化，以高速实现高性能，有效地将其转换为可编程机器人。 该技术还促进了更轻重量机器人系统的设计和部署，从而带来更快的速度，更少的能耗，更少的材料浪费以及其他环境和社会效益。 ***