Time-Invariant, Multi-Objective Extremum Seeking Control for Model-Free Auto-Tuning of Powered Prosthetic Legs

用于动力假肢无模型自动调节的时不变、多目标极值搜索控制

• 批准号: 1728057
• 负责人: Nicholas Gans
• 金额: $ 37.35万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728057&HistoricalAwards=false
• 关键词: Time; Invariant; Multi; Objective; Extremum

This research project seeks fundamental knowledge and understanding of versatile, adaptive optimization methods to enable real-time auto-tuning of powered prosthetic legs. Even with the help of modern prosthetic legs, lower-limb amputees often experience reduced mobility, leading to reduced quality of life and additional health problems. Recently developed powered prosthetic legs have the potential to improve outcomes, but these devices have not been clinically adopted because of the technical expertise and excessive time and effort required to configure their control systems for each patient. These control systems involve dozens of non-intuitive parameters that are specific to each user's physiology, how they walk, and environmental conditions, which also prevents these devices from adapting to the changing rhythms of daily life. Powered prostheses that automatically adjust to changing user activity and environmental conditions could significantly improve mobility for over a million lower-limb amputees in the United States alone. Furthermore, self-tuning could help powered prosthetic legs to adapt to natural changes in the patient perhaps, for example, due to fatigue. The self-tuning algorithms would have applications in control of other repetitive processes, such as powered orthoses for stroke patients, energy-harvesting turbines, HVAC systems, and biological processes. To promote knowledge transfer, the PIs will sponsor senior design projects for undergraduate student teams to design and build new experimental test beds for the developed control systems.The major objective of this research concerns novel methods of model-free adaptive optimization for systems with varying time-scales and competing objectives. Extremum seeking control (ESC) is a powerful approach to model-free adaptive optimization that requires the plant and ESC dynamics to have separated, fixed time-scales in order to optimize a single objective function. However, human locomotion exhibits varying time-scales based on activity (e.g., walking speed) and involves optimization of multiple competing objectives (e.g., energetic efficiency vs. stability). A time-invariant, multi-objective ESC framework is therefore needed to auto-tune powered prosthetic legs, which currently require several hours of customization by an expert, just for baseline operation. The overall goals of this project are to first to understand how to perform ESC of rhythmic processes with varying time-scales for real-time, model-free adaptation, next to understand how to automatically optimize multiple competing objectives using ESC, and, finally, to understand how to auto-tune a powered prosthetic leg for patient-specific behavior without a model of the human user.

本研究项目寻求基础知识和理解的通用，自适应优化方法，使实时自动调整的动力假肢腿。即使在现代假肢的帮助下，下肢截肢者也经常经历行动不便，导致生活质量下降和其他健康问题。最近开发的动力假肢具有改善预后的潜力，但由于技术专长和为每位患者配置控制系统需要花费过多的时间和精力，这些设备尚未被临床采用。这些控制系统涉及几十个非直观的参数，这些参数是针对每个用户的生理、行走方式和环境条件的，这也阻碍了这些设备适应不断变化的日常生活节奏。动力假肢可以自动调整以适应不断变化的使用者活动和环境条件，仅在美国就可以显著改善100多万下肢截肢者的行动能力。此外，自我调节可以帮助动力假肢适应病人的自然变化，例如，由于疲劳。这种自调谐算法还可以应用于其他重复性过程的控制，比如中风患者的动力矫形器、能量收集涡轮机、HVAC系统和生物过程。为促进知识转移，计划资助高年级设计计划，让本科生团队设计和建造新的实验测试平台，以测试已开发的控制系统。本研究的主要目标是研究具有不同时间尺度和竞争目标的系统的无模型自适应优化的新方法。极值寻求控制（ESC）是一种强大的无模型自适应优化方法，它要求对象和ESC动力学具有分离的固定时间尺度，以便优化单个目标函数。然而，人类运动表现出基于活动的不同时间尺度（例如，步行速度），并涉及多个竞争目标的优化（例如，能量效率与稳定性）。因此，需要一个时不变的多目标ESC框架来自动调整动力假肢，目前需要专家进行几个小时的定制，仅用于基线操作。该项目的总体目标是首先了解如何在不同的时间尺度下执行ESC的节奏过程，以实现实时、无模型的适应，其次了解如何使用ESC自动优化多个竞争目标，最后，了解如何在没有人类用户模型的情况下自动调整动力假肢，以适应患者特定的行为。

项目成果

Extremum Seeking Control for Stiffness Auto-Tuning of a Quasi-Passive Ankle Exoskeleton

• DOI: 10.1109/lra.2020.3001541
• 发表时间: 2020-06
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Saurav Kumar;Matthew Richard Zwall;Edgar A. Bolívar-Nieto;R. Gregg;N. Gans

Limit Cycle Minimization by Time-Invariant Extremum Seeking Control

通过时不变极值寻求控制实现极限环最小化

• DOI: 10.23919/acc.2019.8815344
• 发表时间: 2019
• 期刊: Proceedings of the ... American Control Conference
• 作者: Kumar, Saurav;Mohammadi, Alireza;Gregg, Robert D.;Gans, Nicholas
• 通讯作者: Gans, Nicholas