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.

手骑自行车和接触活动是运动障碍患者的康复练习。对于那些没有足够力量自己锻炼的人来说，可以小心地将电流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已被证明具有许多健康益处。先前的研究表明，通过1）重复练习和2）包括FES在内的积极努力，康复得到改善。对于某些人来说，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导致疲劳以比正常更快的速度发生。疲劳可以通过使用FES和机器人的组合来减少（例如，动力自行车或机器人手臂），称为混合外骨骼。例如，只有在最有效的时候才应用FES，只有在需要的时候才让机器人帮助，这将减少疲劳，同时鼓励积极努力。通过自适应地改变FES和机器人在锻炼中的帮助程度，可以进一步减少疲劳。该项目的目标是开发用于控制混合外骨骼的安全自适应方法，这些方法有可能显着改变患有运动障碍的个人的康复。在整个项目中，项目团队将邀请初中和高中学生参加实验室图尔斯和/或实验，评估设计的方法，以激励学生寻求科学，技术，工程和数学（STEM）领域的高等教育。该项目的智力价值来自设计，分析，安全饱和深度神经网络（DNN）的FES控制器与实时闭环的实验演示（基于Lyapunov的）DNN权重更新律，其可以近似上肢混合外骨骼的复杂动力学并保证整体系统稳定性。目标1将开发一种饱和的、并发学习启发的、基于DNN的FES控制律，该控制律在多个时间尺度上更新DNN，并开发一种自适应DNN和基于导纳的运动控制器，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的自适应更新律，而外骨骼的运动控制器将包括障碍函数，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，确定最有前途的控制架构，调查FES控制器降低电机功率要求的潜力，并制定新的外骨骼设计指南。该项目的成功完成可以通过实现对混合外骨骼的更个性化和节能控制来改变康复行业。此外，另一个结果是获得实验数据以使得能够未来开发使用FES来降低外骨骼的重量和成本的无系绳上肢混合外骨骼。该项目由残疾和康复工程计划和刺激竞争研究的既定计划（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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。