ArticleSensors (Basel, Switzerland)2025
Improved Adaptive Sliding Mode Control Using Quasi-Convex Functions and Neural Network-Assisted Time-Delay Estimation for Robotic Manipulators.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Predefined-Time Sliding Mode Control of Robotic Manipulators via Artificial Delay Feedback and Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
This study presents an adaptive sliding mode control strategy tailored for robotic manipulators, featuring a quasi-convex function-based control gain and a time-delay estimation (TDE) enhanced by neural networks. To compensate for TDE errors, the proposed method utilizes both the previous TDE error and radial basis function neural networks with a weight update law that includes damping terms to prevent divergence. Additionally, a continuous gain function that is quasi-convex function dependent on the magnitude of the sliding variable is proposed to replace the traditional switching control gain. This continuous function-based gain has effectiveness in suppressing chattering phenomenon while guaranteeing the stability of the robotic manipulator in terms of uniform ultimate boundedness, which is demonstrated through both simulation and experiment results.
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Registered trials
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