Robot manipulator active fault-tolerant control using a machine learning-based automated robust hybrid observer

Abstract

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.