Sliding Mode Control and Its Applications in Robot Trajectory Tracking Control: Review

Sliding Mode Control (SMC), renowned for its robustness and anti-interference capabilities, is extensively applied in the realm of robot trajectory tracking. This paper reviews the application of SMC in robot trajectory tracking, and compares the performance differences between traditional SMC and improved SMC (including integral sliding mode control ISMC and adaptive neural network sliding mode control ANNSMC). Although traditional SMC is widely used in robot trajectory tracking due to its strong robustness, its high-frequency jitter problem cannot be ignored. In contrast, ISMC effectively reduces the system steady-state error, improves control accuracy and alleviates jitter by introducing an integral term. ANNSMC merges the nonlinear mapping capabilities of neural networks with the resilience of SMC, significantly enhancing trajectory tracking precision. This paper seeks to offer readers a comprehensive view of sliding mode control's application in robot trajectory tracking. It delivers a thorough evaluation of the strengths and weaknesses of different methods, serving as a valuable reference for future research.

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Publication Details

Journal
Applied and Computational Engineering
Published
2024-11-29
DOI
https://doi.org/10.54254/2755-2721/111/2024ch0100
Citations
1
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.20
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article

Sliding Mode Control and Its Applications in Robot Trajectory Tracking Control: Review

Radek Lán
1 citations
Applied and Computational Engineering
Vehicle Dynamics and Control Systems
0.20
article

Sliding Mode Control and Its Applications in Robot Trajectory Tracking Control: Review

Radek Lán
article en
1 citations

Abstract

Sliding Mode Control (SMC), renowned for its robustness and anti-interference capabilities, is extensively applied in the realm of robot trajectory tracking. This paper reviews the application of SMC in robot trajectory tracking, and compares the performance differences between traditional SMC and improved SMC (including integral sliding mode control ISMC and adaptive neural network sliding mode control ANNSMC). Although traditional SMC is widely used in robot trajectory tracking due to its strong robustness, its high-frequency jitter problem cannot be ignored. In contrast, ISMC effectively reduces the system steady-state error, improves control accuracy and alleviates jitter by introducing an integral term. ANNSMC merges the nonlinear mapping capabilities of neural networks with the resilience of SMC, significantly enhancing trajectory tracking precision. This paper seeks to offer readers a comprehensive view of sliding mode control's application in robot trajectory tracking. It delivers a thorough evaluation of the strengths and weaknesses of different methods, serving as a valuable reference for future research.

Applied and Computational EngineeringVol. 111(1)
Beijing Normal-Hong Kong Baptist University (CN), Jinan University (CN)
Openalex Percentile: Top 48%
Vehicle Dynamics and Control Systems
0.20
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