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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Event-Triggered Mixed Nonzero-Sum Game Optimal Control for Modular Robotic Manipulator Performing Coordinated

Tianjiao An, Xiaogang Dong, Bo Dong

IEEE Transactions on Neural Networks and Learning Systems

|August 18, 2025

Summary

This study introduces an event-triggered optimal control method for modular robotic manipulators, enhancing coordination in complex tasks like assembly and transportation. The approach ensures stable and efficient human-robot collaboration by addressing dynamic uncertainties.

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Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Coordinated control of high-performance intelligent robots is a growing research area.
Human-robot interaction presents challenges due to potential conflicts and control inconsistencies.

Purpose of the Study:

To develop an event-triggered mixed nonzero-sum game optimal control method for modular robotic manipulator (MRM) systems.
To address coordination control problems in tasks like assembly, handling, installation, and transportation.

Main Methods:

Established MRM subsystem dynamic models using joint torque feedback and task assignment.
Developed an adaptive neural network (NN) identifier for unknown dynamics and friction errors.
Transformed the coordination problem into a mixed nonzero-sum game using differential game theory.
Approximated optimal value functions and solved event-based Hamiltonian equations using a critic NN.

Main Results:

The adaptive NN identifier ensured convergence of weight errors to a minimal neighborhood of zero.
The developed control strategy yielded optimal control policies for each joint module and the operated object.
Lyapunov theory confirmed the stability of the system under the proposed control method.

Conclusions:

The event-triggered mixed nonzero-sum game optimal control method effectively addresses coordination challenges in MRM systems.
The approach demonstrates robust performance in handling dynamic uncertainties and ensuring system stability.
Experimental validation confirmed the practical effectiveness of the proposed control strategy for coordinated robotic operations.