Safe Lyapunov-Based Deep Neural Network Adaptive Control of a Rehabilitative Upper Extremity Hybrid Exoskeleton

基于安全李亚普诺夫的深度神经网络自适应控制康复上肢混合外骨骼

• 批准号: 2230971
• 负责人: Brendon Allen
• 金额: $ 47.16万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230971&HistoricalAwards=false
• 关键词: Safe; Lyapunov; Based; Deep; Neural

Hand cycling and reaching activities are rehabilitative exercises for individuals with movement disorders. For those with insufficient strength to exercise by themselves, electricity can be carefully applied to a muscle to generate force. This application of electricity is called functional electrical stimulation (FES) and FES has been shown to have many health benefits. Prior research has shown that rehabilitation is improved by 1) repetition of the exercise, and 2) active effort including from FES. For some individuals, weakness and fatigue limit the effectiveness of rehabilitation therapy. Another limitation of FES-based exercise is that FES causes fatigue to occur at a faster rate than normal. Fatigue can be reduced by using a combination of FES and robotics (e.g., a powered cycle, or a robot arm), called hybrid exoskeletons. For example, applying FES only when it is most efficient and having the robot help only when needed will reduce fatigue while encouraging active effort. Fatigue can be further reduced by adaptively changing how much the FES and robot help in the exercise. The goal of this project is to develop safe adaptive methods for controlling hybrid exoskeletons that have the potential to significantly transform the rehabilitation of individuals with movement disorders. Throughout this project, the project team will invite middle and high school students to participate in lab tours and/or experiments that evaluate the designed methods to motivate the students to seek out advanced education in science, technology, engineering, and math (STEM) fields.The intellectual merit of this project arises from the design, analysis, and experimental demonstration of safe saturated deep neural network (DNN)-based FES controllers with real-time closed-loop (Lyapunov-based) DNN weight update laws, which can approximate the complex dynamics of upper extremity hybrid exoskeletons and guarantee overall system stability. Objective 1 will develop a saturated, concurrent learning-inspired, and DNN-based FES control law that updates the DNN in multiple timescales and develop an adaptive DNN- and admittance-based motor controller to improve participant safety. Objective 2 will develop real-time and Lyapunov-based adaptive update laws for both the inner- and output-layer DNN weights, while the exoskeleton's motor controller will include barrier functions to constrain the exoskeleton within a user-defined safe set. Objective 3 will experimentally evaluate the proposed controllers in populations with and without movement disorders, survey participants for user feedback, identify the most promising control architectures, investigate the FES controllers' potential to reduce motor power requirements, and develop new exoskeleton design guidelines. Successful completion of this project could transform the rehabilitation industry by enabling more personalized and energy-efficient control of a hybrid exoskeleton. Moreover, another outcome is to acquire experimental data to enable the future development of an untethered upper extremity hybrid exoskeleton that uses FES to lower the weight and cost of the exoskeleton. This project is jointly funded by the Disability and Rehabilitation Engineering Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

手工骑自行车和到达活动是运动障碍的人的康复练习。对于那些自己锻炼力量不足的人，可以将电力小心地应用于肌肉以产生力。这种电力的应用称为功能电刺激（FES），FES已显示具有许多健康益处。先前的研究表明，通过1）重复练习，改善了康复，以及2）包括FES在内的积极努力。对于某些人来说，软弱和疲劳限制了康复疗法的有效性。基于FES的运动的另一个局限性是FES导致疲劳的发生速度比正常情况快。可以通过使用FES和机器人（例如，动力循环或机器人臂）的组合（称为杂交外骨骼）来减少疲劳。例如，仅在最有效的情况下应用FES，并且只有在需要时才能帮助机器人帮助，这会减少疲劳，同时鼓励积极的努力。可以通过自适应改变FES和机器人在运动中有多大帮助来进一步减少疲劳。该项目的目的是开发安全的自适应方法来控制混合外骨骼，这些方法有可能显着改变运动障碍个体的康复。在整个项目中，项目团队将邀请中学生参加实验室游览和/或实验，以评估设计方法，以激励学生在科学，技术，工程和数学（STEM）领域中寻求高级教育。重量更新定律，可以近似上肢混合外骨骼的复杂动态，并确保整体系统稳定性。目标1将开发出饱和，同时学习的基于DNN的FES控制法，该法律在多个时间尺度上更新DNN，并开发基于自适应的DNN和基于入学的运动控制器，以提高参与者的安全性。目标2将为内部和输出层DNN权重开发实时和基于Lyapunov的自适应更新定律，而外骨骼的电动机控制器将包括屏障功能，以在用户定义的安全集中限制外骨骼。目标3将通过实验评估有或没有运动障碍的人群中提议的控制器，对用户反馈的调查参与者，确定最有前途的控制架构，调查FES控制器减少运动功率需求的潜力，并制定新的外骨骼设计指南。该项目的成功完成可以通过对混合外骨骼进行更个性化和节能的控制来改变康复行业。此外，另一个结果是获取实验数据，以实现未经束缚的上肢混合外骨骼的未来发展，该外骨骼使用FES降低外骨骼的重量和成本。该项目由残疾和康复工程计划和启发竞争性研究的既定计划共同资助。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估来评估的评估。