Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maxillary Sinus Surgery

Abstract

Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi–Sugeno (T–S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T–S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults.

Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer

“…Numerous procedures have been recently presented for FEDI in various systems, which can be divided into four groups: (a) model-based (MB) techniques, (b) signal-based (SB) approaches, (c) knowledge-based (KB) procedures, and (d) hybrid methods [1,4]. Some recently published representative examples of model-based techniques have been reported in [1,[5][6][7]. Model-based techniques for fault diagnosis identify the faults by modeling the system's dynamics using mathematical modeling or system identification techniques using a small dataset.…”

“…The main challenge in designing the procedure of sliding mode observer is system modeling. The mathematical-based system modeling and system identification are the main frameworks for modeling the complex (e.g., bearing) systems [5,6]. In the mathematical-based system modeling, the Lagrange technique can be used for modeling the REBs [6].…”

“…Apart from the reliability and accuracy of physical system modeling, this technique has drawbacks in uncertain and noisy conditions. To address this issue, the system identification techniques are widely applied as indicated in [5,20,21]. To estimate the system using system identification technique, various orthonormal procedures such as Auto Regressive with eXogenous input (ARX), orthonormal function bases (OFB), and generalized orthonormal bases (GOB) methods have been used [20,21].…”

“…Apart from the advantage of complexity reduction in ARX-Laguerre, it has a challenge related to estimation accuracy in nonlinear systems. To increase the accuracy in the ARX-Laguerre technique, the T-S fuzzy ARX-Laguerre (FAL) technique is presented in the literature [5]. In our work, this technique is used to estimate the vibration signals of REBs.…”

“…Despite the improvement of the accuracy obtained from using the FLO, robustness is the main issue of this technique [7]. The extended FLO and SMO have been presented by researchers to improve the robustness of observers [5][6][7]. The SMO is a robust technique for FEDI for nonlinear and complex systems (e.g., rolling bearings) which operate in uncertain and noisy conditions.…”