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: 2040335
• 负责人: Nicholas Gans
• 金额: $ 11.16万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040335&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.

该研究项目寻求对多功能、自适应优化方法的基础知识和理解，以实现动力假肢的实时自动调节。即使在现代假肢的帮助下，下肢截肢者也经常会出现活动能力下降的情况，从而导致生活质量下降和其他健康问题。最近开发的动力假肢有可能改善结果，但这些设备尚未在临床上采用，因为为每个患者配置控制系统需要大量的技术知识和大量的时间和精力。这些控制系统涉及数十个非直观参数，这些参数特定于每个用户的生理机能、行走方式和环境条件，这也阻碍了这些设备适应不断变化的日常生活节奏。自动适应不断变化的用户活动和环境条件的动力假肢可以显着改善仅在美国就超过一百万下肢截肢者的活动能力。此外，自我调节可以帮助动力假肢适应患者的自然变化，例如由于疲劳而引起的变化。自调节算法将应用于控制其他重复过程，例如中风患者的动力矫形器、能量收集涡轮机、暖通空调系统和生物过程。为了促进知识转移，PI 将资助本科生团队的高级设计项目，为已开发的控制系统设计和建造新的实验测试台。这项研究的主要目标是针对不同时间的系统进行无模型自适应优化的新方法-规模和竞争目标。极值搜索控制 (ESC) 是一种强大的无模型自适应优化方法，它要求对象和 ESC 动力学具有独立的固定时间尺度，以便优化单个目标函数。然而，人类运动表现出基于活动（例如步行速度）的不同时间尺度，并且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 框架来自动调节动力假肢，目前仅需要专家进行几个小时的定制才能进行基线操作。该项目的总体目标是首先了解如何对不同时间尺度的节奏过程进行 ESC，以实现实时、无模型的适应，然后了解如何使用 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的特定行为。

项目成果

Multi-Objective Logarithmic Extremum Seeking for Wind Turbine Power Capture with Load Reduction

减少负载的风力发电机功率捕获的多目标对数极值搜索

• DOI: 10.23919/acc50511.2021.9483187
• 发表时间: 2021
• 期刊: Multi-Objective Logarithmic Extremum Seeking for Wind Turbine Power Capture with Load Reduction
• 作者: Kumar, Devesh;Gans, Nicholas;Rotea, Mario A.
• 通讯作者: Rotea, Mario A.

Probabilistic Movement Primitive Control via Control Barrier Functions

• DOI: 10.1109/case49439.2021.9551540
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE 17TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
• 作者: Davoodi, Mohammadreza;Iqbal, Asif;Gans, Nicholas R.
• 通讯作者: Gans, Nicholas R.

Rule-Based Safe Probabilistic Movement Primitive Control via Control Barrier Functions

通过控制屏障函数进行基于规则的安全概率运动原始控制

• DOI: 10.1109/tase.2022.3217468
• 发表时间: 2022
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Davoodi, Mohammadreza;Iqbal, Asif;Cloud, Joseph M.;Beksi, William J.;Gans, Nicholas R.
• 通讯作者: Gans, Nicholas R.

Safe Human-Robot Coetaneousness Through Model Predictive Control Barrier Functions and Motion Distributions

通过模型预测控制障碍函数和运动分布实现安全人机同步

• DOI: 10.1016/j.ifacol.2021.11.186
• 发表时间: 2021
• 期刊: IFAC-PapersOnLine
• 作者: Davoodi, Mohammadreza;Cloud, Joseph M.;Iqbal, Asif;Beksi, William J.;Gans, Nicholas R.
• 通讯作者: Gans, Nicholas R.