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Task-space tracking for networked heterogeneous robotic systems via adaptive neural fixed-time control.

Ren-Jie Gu¹, Tao Han¹, Bo Xiao¹

¹School of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435005, PR China.

ISA Transactions

|October 2, 2024

Summary

This study introduces a novel neural network (NN) control method for networked heterogeneous robotic systems (NHRSs). It ensures fixed-time tracking by addressing uncertainties and improving convergence speed.

Keywords:

Fixed-time hierarchical control (FTHC)Networked heterogeneous robotic systems (NHRSs)Neural networks (NNs)Tracking control

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Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Networked heterogeneous robotic systems (NHRSs) present complex control challenges.
Fixed-time tracking is crucial for precise robotic operations.

Purpose of the Study:

To develop a neural network (NN)-based fixed-time hierarchical control approach for NHRSs.
To address task-space distributed adaptive NN fixed-time tracking problems.

Main Methods:

A hierarchical control strategy decomposing the problem into distributed estimation and local tracking.
Utilizing neural networks (NNs) for approximating compounded uncertainties.
Designing an adaptive neural fixed-time local tracking controller.

Main Results:

Distributed estimators enable followers to acquire leader states in fixed time.
NNs effectively approximate unknown dynamics and disturbances.
The proposed controller ensures fixed-time convergence of tracking errors, independent of initial states.

Conclusions:

The NN-based fixed-time hierarchical control method is effective for NHRSs.
The approach improves robustness and convergence speed by novel handling of approximation errors.
Experimental results validate the proposed control method's advantages.