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Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

机译:基于强大的基于LQR的神经模糊跟踪控制,具有参数化不确定性和外部干扰的下肢外骨骼系统

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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.
机译:由于在步态康复期间,由于不确定的动态和意外主体反射,较低肢体外屏幕系统的精确控制方案的设计具有很少的挑战。在这项工作中,提出了一种强大的基于线性二次调节器 - (LQR-)的神经模糊(NF)控制方案,以解决被动辅助步态培训期间有效载荷不确定性和外部干扰的影响。最初,为耦合系统建立了基于欧拉拉格朗兰的基于基于非线性的非线性动态关系。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)和使用的隶属函数的架构。虽然不同的质量参数高达20%,但是三个强大的神经模糊数据集配制了联合误差矢量和LQR控制输入。此后,为了处理外部干扰,使用射击强度矩阵的在线适应来呈现具有干扰估计器的错误动态。 Lyapunov理论是为了确保鉴于所提出的控制器的偶联的人外骨骼系统的渐近稳定性。提出并与指数到达律滑动模式(ERL-SM)控制器进行了展示了对拟议控制方案(RLQR-NF)的步态跟踪结果。此外,为了研究提出的LQR控制的控制的鲁棒性,提出了两种参数不确定因素和外部干扰的比较性能分析。第一种案例考虑了具有三角形的扰动中质量值的20%,第二种情况包括随机扰动随机造成的质量值30%的效果。模拟运行已经显示了专为被动辅助步态培训的设计控制器的有前途的步态跟踪方面。

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