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

该研究项目寻求通用的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整。即使在现代假肢的帮助下，下肢截肢者也经常遇到行动不便的问题，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，但这些设备还没有被临床采用，因为技术专业知识和过多的时间和精力，需要配置其控制系统为每个病人。这些控制系统涉及数十个非直观的参数，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。自动调整以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有100多万下肢截肢者的活动能力。此外，自我调整可以帮助动力假肢适应病人的自然变化，例如，由于疲劳。自调整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，HVAC系统和生物过程。为促进知识转移，研究所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为具有不同时间尺度和竞争目标的系统提供无模型自适应优化的新方法。寻轨控制（ESC）是无模型自适应优化的一种强有力的方法，它要求被控对象和ESC动态具有分离的、固定的时间尺度，以便优化单个目标函数。然而，人类运动基于活动表现出不同的时间尺度（例如，步行速度）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执行具有不同时间尺度的节奏过程的ESC，以实现实时、无模型适应，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针对患者特定行为自动调整动力假肢。

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.