级联描述符观察者:应用于理解脊髓损伤患者的坐姿控制

Hajer Srihi, T. Guerra, Anh‐Tu Nguyen, P. Pudlo, A. Dequidt
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引用次数: 2

摘要

脊髓损伤(SCI)患者的坐姿稳定性急剧下降,这对他们的姿势控制产生了负面影响。因此,坐姿平衡成为最具挑战性的日常锻炼之一。为了更好地理解这种病理学的后果,我们必须使用高尺寸的非线性生物力学模型,这意味着理论和数值上的困难。主要目标是恢复未测量的输入,观测器应该有有限的简化或根本没有简化,以提供更好的估计质量。设计了一个比例积分观测器(PI观测器),通过凸优化技术,利用线性矩阵不等式(LMI)来表示其收敛性。使用一个独特的大尺寸观测器,LMI约束问题可以快速达到当前求解器在所需未知参数数量方面的限制。解决这个问题的一种方法是设计一个级联观测器,以估计SCI患者的不可测量扭矩。这种方法包括将生物力学模型分解为相互连接的子系统,并建立“局部”观察者。该方法的相关性在仿真和实时实验数据中得到了证明。
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Cascade Descriptor Observers: Application to Understanding Sitting Control of Persons Living With Spinal Cord Injury
People with spinal cord injury (SCI) suffer from a drastic reduction in sitting stability which negatively impacts their postural control. Thus, sitting balance becomes one of the most challenging everyday exercises. To better understand the consequences of this pathology, we have to work with high-sized non-linear biomechanical models implying both theoretical and numerical difficulties. The main goal being to recover unmeasured inputs, the observer should have limited or no simplification at all to provide a better estimation quality. A Proportional Integral-observer (PI-observer) is designed and its convergence is formulated by linear matrix inequalities (LMI) through convex optimization techniques. Using a unique high-sized observer, the LMI constraints problem can quickly reach current solvers limitations regarding the number of unknown parameters required. A way to solve this issue is to design a cascade observer in order to estimate the unmeasurable torques of a human with SCI. This approach consists in decomposing a biomechanical model into interconnected subsystems and to build “local” observers. The relevance of this approach is demonstrated in simulation and with real-time experimental data.
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