Neurological disorders such as cerebral paralysis, spinal cord injuries[acronym](SCI), and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging [english]affectsaffect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) [acronym](ADRC) control strategy is a systematic way out from [english]a robust control paradox with possibilities and promises. In this modern era, we always try to find [english]the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition [english]observer basedobserver-based control strategies is an added advantage where it uses a state estimation method which [english]in tern reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as [english]a combination of linear extended state observer (LESO), tracking differentiator (TD)[english], and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two [english]degreedegrees of freedom[english], Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at [english]the hip and other at [english]the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant[english], and harmonic disturbances, robustness against parameter variations[english], and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research.

中文翻译：

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改进的主动干扰抑制控制，用于下肢机器人康复外骨骼的轨迹跟踪控制。

神经系统疾病，例如脑瘫，脊髓损伤（SCI）和中风，会导致运动控制受损，并给人类造成步行，站立等功能障碍。事故和肌肉无力引起的身体伤害衰老会影响人们，并可能使他们失去执行日常功能的能力。为了帮助人们在事故或中风后恢复或改善其功能障碍的活动和生活质量，开发了诸如骨骼外骨骼和矫形器之类的辅助装置。已经开发出用于控制外骨骼的控制策略，以改善治疗质量。在用于康复机器人的最新控制策略中，主动干扰抑制控制（ADRC）[ADRC]控制策略是摆脱具有鲁棒性和可能性的鲁棒控制悖论的系统方法。在这个现代时代，我们总是试图找到一种解决方案，以便拥有最少的资源和最大的产出，并且在机器人控制中，要达到相同的条件，基于观察者的基于控制者的控制策略是一个额外的优势，其中它使用状态估计方法，从而减少了用于测量每个状态的传感器的需求。本文介绍了改进的有源干扰抑制控制（I-ADRC）控制器，它们是线性扩展状态观测器（LESO），跟踪微分器（TD）和非线性状态误差反馈（NLSEF）的组合。通过研究两个自由度（英语）下肢机器人康复外骨骼（LLRRE）的矢状面步态轨迹跟踪性能，通过仿真对拟议的控制器进行了评估。这种多输入多输出（MIMO）LLRRE具有两个关节，一个在[英语]臀部，另一个在[英语]膝盖。在仿真研究中，所提出的控制器显示出减小的轨迹跟踪误差，消除了随机，恒定和谐波干扰，对参数变化具有鲁棒性，并且在噪声的影响下，其性能指标有所提高，表明了其性能。增强跟踪性能。这些有希望的模拟结果将在下一阶段的研究中通过实验验证。