Nonlinear model predictive control of joint ankle by electrical stimulation for drop foot correction
Nonlinear model predictive control of joint ankle by electrical stimulation for drop foot correction
复制标题
电刺激踝关节非线性模型预测控制矫正足下垂
DOI：
10.1109/iros.2013.6696470
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
C. Azevedo
中科院分区：
文献类型：
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作者：
Mourad Benoussaad;K. Mombaur;C. Azevedo
在本文中，我们研究了如何使用最优控制技术来改善功能性电刺激 (FES)，以矫正偏瘫患者的足下垂。建立了足部和胫骨前肌的模型，其收缩由电刺激控制，并用于最优控制问题。这项工作的新颖之处在于模型中使用了脚踝加速度和小腿方向（所谓的外部状态），这些加速度和小腿方向（所谓的外部状态）已在之前的实验中使用惯性测量单元（IMU）对偏瘫患者进行了测量。最优控制问题最小化肌肉兴奋的平方，这服务于减少肌肉能量消耗的总体目标。第一步，出于测试目的解决离线最优控制问题，并显示 FES 最优控制对于下垂矫正的效率。第二步，在模拟环境中解决非线性模型预测控制 (NMPC) 问题 - 或在线最优控制问题。虽然最终目标是在真实系统上使用 NMPC，即直接在患者身上使用，但该模拟测试旨在展示 NMPC 用于在线足下垂矫正的可行性。在优化问题中，应用了一组脚方向的固定约束。然后，引入并测试了考虑当前脚踝高度的原始自适应约束。固定约束和自适应约束下的结果之间的比较突出了自适应约束在能耗方面的优势，其中通过 NMPC 获得的控制二次和比固定约束低三倍。这项可行性研究是NMPC应用于真实偏瘫患者进行基于FES的在线足下垂矫正的第一步。自适应约束方法在肌肉能量消耗最小化方面提出了一种新的有效方法。
In this paper we investigate the use of optimal control techniques to improve Functional Electrical Stimulation (FES) for drop foot correction on hemiplegic patients. A model of the foot and the tibialis anterior muscle, the contraction of which is controlled by electrical stimulation has been established and is used in the optimal control problem. The novelty in this work is the use of the ankle accelerations and shank orientations (so-called external states) in the model, which have been measured on hemiplegic patients in a previous experiment using Inertial Measurement Units (IMUs). The optimal control problem minimizes the square of muscle excitations which serves the overall goal of reducing energy consumption in the muscle. In a first step, an offline optimal control problem is solved for test purposes and shows the efficiency of the FES optimal control for drop foot correction. In a second step, a Nonlinear Model Predictive Control (NMPC) problem - or online optimal control problem, is solved in a simulated environment. While the ulitmate goal is to use NMPC on the real system, i.e. directly on the patient, this test in simulation was meant to show the feasibility of NMPC for online drop foot correction. In the optimization problem, a set of fixed constraints of foot orientation was applied. Then, an original adaptive constraint taking into account the current ankle height, was introduced and tested. Comparisons between results under fixed and adaptive constraints highlight the advantage of the adaptive constraints in terms of energy consumption, where its quadratic sum of controls, obtained by NMPC, was three times lower than with the fixed constraint. This feasibility study was a first step in application of NMPC on real hemiplegic patients for online FES-based drop foot correction. The adaptive constraints method presents a new and efficient approach in terms of muscular energy consumption minimization.