The design of an accurate control scheme for a lower limb exoskeleton system has few challenges due to the uncertain dynamics and the unintended subject’s reflexes during gait rehabilitation. In this work, a robust linear quadratic regulator- (LQR-) based neural-fuzzy (NF) control scheme is proposed to address the effect of payload uncertainties and external disturbances during passive-assist gait training. Initially, the Euler-Lagrange principle-based nonlinear dynamic relations are established for the coupled system. The input-output feedback linearization approach is used to transform the nonlinear relations into a linearized state-space form. The architecture of the adaptive neuro-fuzzy inference system (ANFIS) and used membership function are briefly explained. While varying mass parameters up to 20%, three robust neural-fuzzy datasets are formulated offline with the joint error vector and LQR control input. Thereafter, to deal with external interferences, an error dynamics with a disturbance estimator is presented using an online adaptation of the firing strength matrix. The Lyapunov theory is carried out to ensure the asymptotic stability of the coupled human-exoskeleton system in view of the proposed controller. The gait tracking results for the proposed control scheme (RLQR-NF) are presented and compared with the exponential reaching law-based sliding mode (ERL-SM) controller. Furthermore, to investigate the robustness of the proposed control over LQR control, a comparative performance analysis is presented for two cases of parametric uncertainties and external disturbances. The first case considers the 20% raise in mass values with a trigonometric form of disturbances, and the second case includes the effect of the 30% increment in mass values with a random form of disturbances. The simulation runs have shown the promising gait tracking aspects of the designed controller for passive-assist gait training.

中文翻译：

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具有参数不确定性和外部干扰的下肢外骨骼系统基于 LQR 的鲁棒神经模糊跟踪控制


由于步态康复过程中动力学的不确定性和受试者的无意识反射，下肢外骨骼系统的精确控制方案的设计几乎没有挑战。在这项工作中，提出了一种基于鲁棒线性二次调节器（LQR）的神经模糊（NF）控制方案，以解决被动辅助步态训练期间有效负载不确定性和外部干扰的影响。首先，为耦合系统建立了基于欧拉-拉格朗日原理的非线性动态关系。输入输出反馈线性化方法用于将非线性关系转换为线性化状态空间形式。简要解释了自适应神经模糊推理系统（ANFIS）的体系结构和使用的隶属函数。当质量参数变化高达 20% 时，三个鲁棒的神经模糊数据集通过联合误差向量和 LQR 控制输入离线制定。此后，为了处理外部干扰，使用发射强度矩阵的在线自适应来呈现干扰估计器的误差动态。鉴于所提出的控制器，李雅普诺夫理论被用来确保耦合的人体-外骨骼系统的渐近稳定性。提出了所提出的控制方案（RLQR-NF）的步态跟踪结果，并与基于指数趋近律的滑模（ERL-SM）控制器进行了比较。此外，为了研究所提出的控制相对于 LQR 控制的鲁棒性，对参数不确定性和外部干扰的两种情况进行了性能比较分析。 第一种情况考虑了具有三角形式扰动的质量值增加 20%，第二种情况包括具有随机形式扰动的质量值增加 30% 的影响。模拟运行显示了所设计的用于被动辅助步态训练的控制器在步态跟踪方面的前景。