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Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation.

Yunsong Hu, Huaicheng Yan, Hao Zhang

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|April 20, 2022

Summary

This study introduces a robust adaptive fixed-time sliding-mode control for uncertain robotic systems. The method enhances control performance and convergence speed, demonstrating superior efficacy in simulations.

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Area of Science:

Robotics
Control Systems Engineering
Artificial Intelligence

Background:

Robotic systems often face challenges like parameter uncertainties and input saturation.
Existing control methods may struggle with rapid convergence and robustness against complex disturbances.

Purpose of the Study:

To propose a robust adaptive fixed-time sliding-mode control method for robotic systems.
To enhance the robustness and convergence speed of robotic systems under uncertainty and saturation.

Main Methods:

Designed a model-based fixed-time controller assuming known system parameters.
Approximated compounded uncertainty using Gaussian radial basis function neural networks (NNs).
Incorporated nonsingular fast terminal sliding-mode (NFTSM) control for improved performance.

Main Results:

The proposed controller effectively handles parameter uncertainties and input saturation.
Neural networks accurately approximated system dynamics and disturbances.
NFTSM integration significantly boosted robustness and convergence speed.

Conclusions:

The developed adaptive fixed-time sliding-mode control method is effective for uncertain robotic systems.
Comparative simulations confirm the superiority and efficacy of the proposed approach over existing methods.