Collaborative Research: Adaptive Control and Functional Electrical Stimulation for the Control and Understanding of Muscle Dynamics.

CBET-0828114 Leonessa功能性电刺激（FES）是一种神经假体技术，用于恢复脊髓损伤（SCI）患者的运动功能。对于SCI患者，在损伤水平以下仍有一些肌肉受到神经支配，尽管不是意志可控的。FES的原理是使用表面或可植入电极在完整的运动神经元中产生电流脉冲。这样做是为了引起这些肌肉的收缩和相应的关节运动。一些挑战阻碍了闭环FES在研究实验室之外的应用，例如肌肉呈现高度非线性和时变特性。此外，当疲劳发生时，受刺激的肌肉发生变化，并且肌肉模型对于每种肌肉类型是不同的。更具有挑战性的是，刺激和肌肉收缩之间存在显著延迟，增加了电刺激系统中的处理和传输延迟。在这个项目中的研究工作将集中在开发一个模型参考自适应控制方法，利用神经网络的逼近能力。这种方法解决了几个开放的问题，包括不确定和未建模的动态，致动器动态，致动器的幅度和速率饱和，延迟，离散和实时实现。使用肌肉驱动的下肢前向动态模型进行测试的控制算法，使用OpenSim，公开可用的肌肉骨骼建模和仿真软件环境。一个实验装置的开发，以测试，理解和比较肌肉动力学在开放和闭环的情况下。这种努力的优点包括扩展当前的非线性控制技术，如反推，动态表面，和模型参考自适应控制，以考虑时间延迟，致动器幅度和速率饱和限制，以及部分和噪声测量，从而大大增加了这种算法的实用性。FES设备的实时实现以及治疗师和患者易于设置和简单使用的要求为控制结构增加了额外的约束，该控制结构需要是鲁棒的但不过于复杂。控制工程师开发的先进控制技术以前没有与先进的神经肌肉骨骼模型和临床医生和生物力学家的生物学理解相结合。这样做将增加对肌肉特征的了解，并可能导致开发一种增强型假肢系统。这项研究工作为社会提供了许多潜在的好处，包括改善瘫痪患者生活质量的可能性，以及其他神经肌肉残疾的个体，包括创伤性脑损伤，多发性硬化症和脑瘫。与肌肉驱动的运动模拟合作开发一个强大的控制策略将为指导特定损伤的康复策略提供一个框架。通过未来与维克森林医学院的康复研究人员和临床医生的潜在合作，作为这项工作的一部分开发的技术将应用于特定的患者群体。从教育的角度来看，来自机械工程和人类营养，食品和运动部门以及生物医学工程的学生将共同努力，在讲座和实验室中学习和体验闭环控制技术在生物工程问题中的应用，增强他们对这一领域的兴趣。生物工程的跨学科方面将通过课程开发和推广得到覆盖和传播。

CBET-0828114LeonessaFunctional electrical stimulation (FES) is a neuroprosthesis technique used to restore the motor function of individuals with spinal cord injuries (SCI). For SCI patients, there are some muscles below the injury level which are still innervated, though not volitionally controllable. The principle of FES is to use surface or implantable electrodes to generate pulses of current in intact motor neurons. This is done to induce contraction of these muscles and corresponding joint movement. Several challenges hinder the application of closed-loop FES outside of research labs, such as that muscles present highly nonlinear and time-varying characteristics. Furthermore, a stimulated muscle changes when fatigue occurs, and muscle models are different for each individual type of muscle. Even more challenging is the fact that there is a significant delay between stimulation and muscle contraction, adding to the processing and transmission delays in the electrical stimulation system. Research efforts in this project will focus on the development a Model Reference Adaptive Control approach which utilizes the approximation capabilities of Neural Networks. This approach addresses several open problems including uncertain and unmodeled dynamics, actuator dynamics, actuator amplitude and rate saturations, delays, and discrete and real-time implementation. The control algorithm is tested using a muscle-driven forward dynamic model of the lower limb as implemented using OpenSim, a publicly-available musculoskeletal modeling and simulation software environment. An experimental setup is developed to test, understand, and compare muscle dynamics in both open and closed-loop situations. The merit of this effort includes the extension of current nonlinear control techniques, such as backstepping, dynamic surfacing, and model reference adaptive control, to account for time delays, actuator amplitude and rate saturation limitations, and partial and noisy measurements, thereby substantially increasing the practical applicability of such algorithms. The real-time implementation and the requirement that the FES equipment is easy to setup and simple to use by therapists and patients add additional constraints to the control structure, which needs to be robust yet not overly complicated. Advanced control techniques developed by control engineers have not previously been merged with the advanced neuromusculoskeletal models and the biological understanding of clinicians and biomechanists. Doing so will increase knowledge of muscle characteristics and could lead to the development of an enhanced prosthetic system. This research effort offers many potential benefits to society, including the possibility of improving the quality of life for patients with paralysis, as well as individuals with other neuromuscular disability including traumatic brain injury, multiple sclerosis, and cerebral palsy. Development of a robust control strategy in cooperation with muscle-driven simulations of movement will provide a framework for guiding rehabilitation strategies for specific impairments. Through a potential future collaboration with rehabilitation researchers and clinicians on the Wake Forest medical campus, the techniques developed as part of this effort will be applied to specific patient populations. From an educational point of view, students from the Mechanical Engineering and Human Nutrition, Foods and Exercise Departments, and Biomedical Engineering will work together to learn and experience in lectures and labs the application of closed loop control techniques to bioengineering problems, bolstering their interest in this field. The interdisciplinary aspects of bioengineering will be covered and disseminated through course development and outreach.