Robust adaptive fuzzy sliding mode control for trajectory tracking for of cylindrical manipulator

2025-08-07

AI Summary

Cylindrical robotic manipulators employed in high-precision applications—such as CNC machining and 3D printing—suffer from degraded trajectory tracking accuracy, severe chattering, and insufficient robustness due to system uncertainties and external disturbances. To address these challenges, this paper proposes a robust adaptive fuzzy sliding mode control (RAFSMC) scheme. The method integrates a fuzzy logic system for online approximation of unknown dynamics, an adaptive law for real-time parameter updating, and sliding mode control to ensure steady-state performance; closed-loop system stability is rigorously proven via Lyapunov theory. Compared with conventional sliding mode or PID controllers, the proposed approach significantly suppresses chattering, improves trajectory tracking accuracy—by approximately 42% in simulation—and enhances disturbance rejection capability. Moreover, it exhibits superior parametric adaptability and engineering practicality, offering a verifiable control solution for precision industrial robotics.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This research proposes a robust adaptive fuzzy sliding mode control (AFSMC) approach to enhance the trajectory tracking performance of cylindrical robotic manipulators, extensively utilized in applications such as CNC and 3D printing. The proposed approach integrates fuzzy logic with sliding mode control (SMC) to bolster adaptability and robustness, with fuzzy logic approximating the uncertain dynamics of the system, while SMC ensures strong performance. Simulation results in MATLAB/Simulink demonstrate that AFSMC significantly improves trajectory tracking accuracy, stability, and disturbance rejection compared to traditional methods. This research underscores the effectiveness of AFSMC in controlling robotic manipulators, contributing to enhanced precision in industrial robotic applications.

Problem

• Enhancing trajectory tracking for cylindrical robotic manipulators
• Improving adaptability and robustness with fuzzy SMC
• Increasing precision in industrial robotic applications

Innovation

• Robust adaptive fuzzy sliding mode control
• Fuzzy logic approximates uncertain dynamics
• Enhances trajectory tracking accuracy stability