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

这项研究项目寻求基础知识和对通用、自适应优化方法的理解，以实现电动假肢的实时自动调谐。即使在现代假肢的帮助下，下肢截肢者往往行动不便，导致生活质量下降和额外的健康问题。最近开发的电动假肢具有改善预后的潜力，但由于技术专业知识和为每个患者配置控制系统所需的过多时间和精力，这些设备尚未在临床上采用。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、他们如何行走以及环境条件，这也阻止了这些设备适应不断变化的日常生活节奏。电动假肢可以根据不断变化的用户活动和环境条件进行自动调整，仅在美国就可以显著改善100多万名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患者的自然变化，例如，由于疲劳。自校正算法将应用于控制其他重复过程，如中风患者的电动矫形器、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，PIS将资助本科生团队的高级设计项目，为开发的控制系统设计和建造新的实验试验台。这项研究的主要目标是针对不同时间尺度和相互竞争的目标的系统的无模型自适应优化的新方法。极值寻优控制(ESC)是一种有效的无模型自适应优化方法，它要求被控对象和ESC动态具有分离的、固定的时间尺度，以优化单个目标函数。然而，人类运动基于活动(例如，行走速度)表现出不同的时间尺度，并且涉及多个相互竞争的目标的优化(例如，能量效率与稳定性)。因此，需要一个不变的、多目标的ESC框架来自动调整动力假肢，目前仅为基线操作就需要专家定制几个小时。该项目的总体目标是首先了解如何执行具有不同时间尺度的节奏过程的ESC以实现实时、非模型适应，其次了解如何使用ESC自动优化多个相互竞争的目标，最后了解如何在没有人类用户模型的情况下针对患者特定的行为自动调整电动假肢。

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.