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.

手骑自行车和伸展活动是运动障碍患者的康复练习。对于那些没有足够力量独自运动的人，可以小心地将电施加到肌肉上以产生力量。这种电的应用被称为功能性电刺激(FES)，FES已被证明对健康有许多好处。先前的研究表明，1)重复锻炼，2)积极努力，包括FES，可以改善康复。对一些人来说，虚弱和疲劳限制了康复治疗的有效性。基于FES的运动的另一个限制是FES导致疲劳发生的速度比正常情况下更快。疲劳可以通过使用FES和机器人技术(例如，电动自行车或机械臂)的组合来减少，称为混合外骨骼。例如，只有在最有效的时候才使用FES，只有在需要的时候才让机器人帮助，这将在鼓励积极努力的同时减少疲劳。通过自适应地改变FES和机器人在练习中的帮助程度，可以进一步减少疲劳。该项目的目标是开发安全的适应性方法来控制混合外骨骼，这种外骨骼有可能显著改变运动障碍患者的康复。在整个项目中，项目组将邀请初中生参加实验室参观和/或实验，以评估所设计的方法，以激励学生寻求科学、技术、工程和数学(STEM)领域的高级教育。本项目的智力优势源于基于安全饱和深度神经网络(DNN)的FES控制器的设计、分析和实验演示，该控制器具有实时闭环(基于Lyapunov)的DNN权重更新规律，可以近似上肢混合外骨骼的复杂动力学，并保证系统整体稳定性。目标1将开发一种饱和的、受并发学习启发的、基于DNN的FES控制律，它在多个时间尺度上更新DNN，并开发一种基于DNN和导纳的自适应电机控制器，以提高参与者的安全性。目标2将为内层和输出层DNN权重开发实时和基于Lyapunov的自适应更新规律，而外骨骼的运动控制器将包括屏障功能，以将外骨骼约束在用户定义的安全集内。目标3将在有和没有运动障碍的人群中对所提出的控制器进行实验评估，调查参与者的用户反馈，确定最有希望的控制架构，调查FES控制器降低运动功率需求的潜力，并开发新的外骨骼设计指南。该项目的成功完成可以通过实现对混合外骨骼的更个性化和更节能的控制来改变康复行业。此外，另一个成果是获得实验数据，使未来能够开发一种使用FES来降低外骨骼重量和成本的无绳式上肢混合外骨骼。该项目由残疾和康复工程计划和既定的激励竞争性研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。