This article presents an estimator-based nonlinear robust optimal controller for an active prosthetic leg for transfemoral amputees. The proposed controller is derived from a combination of the state-dependent Riccati equation technique to optimize the energy consumption of the robot/prosthesis system and the sliding mode control to reduce the effects of the model parametric uncertainties and ground reaction forces as nonparametric uncertainties. In addition, the integral state control technique is employed to improve tracking; also, to have a compromise between tracking performance and control signal chattering, the boundary layer is then used. In this study, the performance of both the controller and estimator in the presence of noise and disturbance is assessed for nominal system while ±40% parametric uncertainty with respect to saturation bounds of control signals is considered. The results of the simulation in this research with ±40% parametric uncertainty compared to a robust adaptive impedance control approach with the only variation of ±30% on the system parameters, show high performance of the proposed controllers in reducing energy consumption, good robustness, improved position tracking performance, and good performance in estimating state variables, even in the presence of large initial errors compared to the extended Kalman filter.

中文翻译：

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用于有源经股假体的非线性鲁棒最优控制器：基于估计器的状态相关 Riccati 方程方法

本文提出了一种基于估计器的非线性鲁棒最优控制器，用于经股截肢者的活动假腿。所提出的控制器源自状态相关 Riccati 方程技术的组合，以优化机器人/假肢系统的能耗和滑模控制，以减少模型参数不确定性和地面反作用力作为非参数不确定性的影响。此外，采用积分状态控制技术来改善跟踪；此外，为了在跟踪性能和控制信号抖动之间取得折衷，然后使用边界层。在这项研究中，针对标称系统评估控制器和估计器在存在噪声和干扰的情况下的性能，同时考虑控制信号饱和边界的 ±40% 参数不确定性。本研究中的仿真结果具有 ±40% 的参数不确定性，与系统参数仅有 ±30% 变化的稳健自适应阻抗控制方法相比，表明所提出的控制器在降低能耗、良好的稳健性、与扩展卡尔曼滤波器相比，改进了位置跟踪性能，并在估计状态变量方面具有良好的性能，即使在存在较大初始误差的情况下也是如此。