A Novel High-Speed Third-Order Sliding Mode Observer for Fault-Tolerant Control Problem of Robot Manipulators

Nguyen, Van-Cuong; Tran, Xuan-Toa; Kang, Hyejin

doi:10.3390/act11090259

Cited by 9 publications

(4 citation statements)

References 49 publications

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“…In this section, a modified TOSMO is designed to estimate the whole interior uncertainty and exterior disturbance. This observer is designed based on the modification of TOSMO [ 32 ] to improve the convergence speed of TOSMO as follows:

where

, and

are the estimated value of

, and

, respectively.

represents observer gain which is selected as [ 33 ], and

is a design positive constant.…”

Section: Design Of the Proposed Control Methodsmentioning

confidence: 99%

Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems

Truong

Kang

2022

Sensors

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For magnetic levitation systems subject to dynamical uncertainty and exterior perturbations, we implement a real-time Prescribed Performance Control (PPC). A modified function of Global Fast Terminal Sliding Mode Manifold (GFTSMM) based on the transformed error of the novel PPC is introduced; hence, the error variable quickly converges to the equilibrium point with the prescribed performance, which means that maximum overshoot and steady-state of the controlled errors will be in a knowledge-defined boundary. To enhance the performance of Global Fast Terminal Sliding Mode Control (GFTSMC) and to reduce chattering in the control input, a modified third-order sliding mode observer (MTOSMO) is proposed to estimate the whole uncertainty and external disturbance. The combination of the GFTSMC, PPC, and MTOSMO generates a novel solution ensuring a finite-time stable position of the controlled ball and the possibility of performing different orbit tracking missions with an impressive performance in terms of tracking accuracy, fast convergence, stabilization, and chattering reduction. It also possesses a simple design that is suitable for real-time applications. By using the Lyapunov-based method, the stable evidence of the developed method is fully verified. We implement a simulation and an experiment on the laboratory magnetic levitation model to demonstrate the improved performance of the developed control system.

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where

, and

are the estimated value of

, and

, respectively.

represents observer gain which is selected as [ 33 ], and

is a design positive constant.…”

Section: Design Of the Proposed Control Methodsmentioning

confidence: 99%

Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems

Truong

Kang

2022

Sensors

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“…However, despite their effectiveness in handling complex systems, FLC approaches face significant challenges in stability analysis and require a comprehensive understanding of system requirements. Sliding mode control (SMC) is a great, robust controller that has been broadly employed for FTC due to its efficiency in eliminating the impacts of unknown input [41][42][43]. It can also resolve the two major challenging problems in controlling them, which are system stability and robustness.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

Tran,

Nguyen,

et al. 2024

Actuators

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In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

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“…The process entails detecting and pinpointing anomalies or malfunctions in the system's constituent parts or functions. In the realm of high-speed computing systems, the issue of fault diagnosis assumes heightened importance owing to the substantial ramifications that faults can exert on the system's operational efficiency and overall efficacy [7][8]. Multiple factors prompt the requirement for fault diagnosis in high-speed computing systems.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in high-speed computing systems using big data analytics integrated evolutionary computing on the Internet of Everything platform

P.Jothi

2024

jes

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The significance of high-speed computing systems is paramount for diverse applications. However, their intricate structure and rigorous operational prerequisites render them susceptible to malfunctions. The conventional fault diagnosis models need help managing the copious data produced by these systems, resulting in diagnostic procedures that are both time-consuming and ineffective. The research proposed an Internet of Everything (IoE) platform and big data analytics (BDA) based Fault Diagnosis System (IBFDS) in conjunction with evolutionary computing techniques. The system integrates a hybrid intelligent algorithm that amalgamates the advantages of evolutionary computing and machine learning to enhance the precision and effectiveness of fault diagnosis. The utilization of big data analysis methodologies facilitates the processing and examination of vast quantities of system data, thereby enabling the comprehensive detection and identification of faults. Furthermore, cloud services based on the IoE allow data storage, administration, and instantaneous cooperation among various parties involved. Furthermore, a detection technique utilizing a Reduced Boltzmann Machine (RBoM) improves the precision of fault diagnosis. The efficacy of the proposed IBFDS was demonstrated through experimental evaluation using a high-speed computing system dataset. The results obtained were impressive, with a fault detection rate (94.11%), fault identification accuracy (93.76%), fault localization accuracy (93.71%), false positive rate (1.54%), and false negative rate (0.98%). The findings of this study make a valuable contribution to the advancement of fault diagnosis systems that are resilient and effective for high-speed computing systems. This facilitates proactive maintenance, enhances system reliability, and optimizes system performance.

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A Novel High-Speed Third-Order Sliding Mode Observer for Fault-Tolerant Control Problem of Robot Manipulators

Cited by 9 publications

References 49 publications

Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems

Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

Fault diagnosis in high-speed computing systems using big data analytics integrated evolutionary computing on the Internet of Everything platform

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