Abstract
In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy sliding mode extended autoregressive exogenous input (ARX)–Laguerre proportional integral (PI) observer is proposed. The proposed T–S fuzzy sliding mode extended-state ARX–Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system’s stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T–S fuzzy sliding mode estimation extended-state ARX–Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.
Highlights
Robot manipulators are often used in various industries, such as packaging and automotive manufacturing
The dynamic of the 6-degrees of freedom (DOF) programmable universal manipulation arm (PUMA) robot manipulator was modeled in Reference [1]
The desired signals are defined based on the cubic polynomials of the third order function and are written using the following equations:
Summary
Robot manipulators are often used in various industries, such as packaging and automotive manufacturing. A robot manipulator’s dynamic behavior is entirely nonlinear, coupled, and time-variant, which causes several challenges in fault detection, estimation, identification, and tolerant control. Several types of fault diagnosis and fault-tolerant control algorithms have been developed for robot manipulators. These methods are divided into four main classes: (a) Signal-based,. (b) model-reference, (c) knowledge-based, and (d) hybrid techniques [4,5,6]. All methods for fault diagnosis have specific advantages and challenges. Knowledge-based fault diagnosis is highly dependent on the historical data used for training, which incur high computational costs for real-time data. The model-reference method identifies faults using a small dataset, but it requires an accurate system model [7].
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