Robotic manipulators represent a class of nonlinear and multiple-degrees-of-freedom robots that have pronounced coupling effects and can be used in various applications. The challenge of understanding complexity in a system’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regarding fault estimation, detection, identification, and tolerant-control (FEDIT) in a robot manipulator. Thus, a proposed active fault-tolerant control algorithm, based on an adaptive modern sliding mode observer, is represented. Due to the effect of the system’s complexities and uncertainties for fault estimation, detection, and identification (FEDI), a sliding mode observer (SMO) is proposed. To address the sliding mode observer drawbacks for FEDI such as high-frequency oscillation (chattering) and fault estimation accuracy, the modern (T-S fuzzy higher order) technique is represented. In addition, the adaptive technique is applied to the modern sliding mode observer (MSMO) to self-tune the coefficients of the fault estimation observer to increase the reliability and robustness of decision-making for diagnosis of the fault. Next, the residual delivered by the adaptive MSMO (AMSMO) is split into windows, and each window is characterized by a numerical parameter. Finally, the machine learning technique known as a decision tree adaptively derives the threshold values that are used for problems of fault detection and fault identification in this work. Due to control of the effective fault, a surface automated new sliding mode controller (SANSMC) is presented in this work. To address the challenge of chattering and unlimited uncertainties (faults), the AMSMO is applied to the sliding mode controller (SMC). In addition, the surface-automated technique is used to fine-tune the surface coefficient to reduce the chattering and faults in the robot manipulator. The results show that the machine learning-based automated robust hybrid observer significantly improves the robustness, reliability, and accuracy of FEDIT in unknown conditions.

中文翻译：

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机器人操纵器主动容错控制，使用基于机器学习的自动鲁棒混合观测器

机器人操纵器代表一类非线性和多自由度机器人，具有明显的耦合作用，可用于各种应用中。理解系统动态行为，耦合效应和不确定性来源的复杂性的挑战提出了与机器人操纵器中的故障估计，检测，识别和公差控制（FEDIT）有关的重大挑战。因此，表示了一种基于自适应现代滑模观测器的主动容错控制算法。由于系统的复杂性和不确定性对故障估计，检测和识别（FEDI）的影响，提出了一种滑模观测器（SMO）。为了解决FEDI的滑模观测器缺点，例如高频振荡（抖动）和故障估计精度，代表了现代（TS模糊高阶）技术。另外，将自适应技术应用于现代滑模观测器（MSMO），以自调整故障估计观测器的系数，以提高诊断故障的决策的可靠性和鲁棒性。接下来，将自适应MSMO（AMSMO）传递的残差拆分为多个窗口，并通过数字参数来表征每个窗口。最后，被称为决策树的机器学习技术自适应地得出阈值，该阈值用于这项工作中的故障检测和故障识别问题。由于有效故障的控制，本文提出了一种表面自动化的新型滑模控制器（SANSMC）。为了应对of不休和无限不确定性（故障）的挑战，AMSMO应用于滑模控制器（SMC）。此外，表面自动化技术用于微调表面系数，以减少机器人操纵器中的颤动和故障。结果表明，基于机器学习的自动鲁棒混合观测器可显着提高未知条件下FEDIT的鲁棒性，可靠性和准确性。