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Updated: Jun 3, 2026

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

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

Published on: October 14, 2017

Learning-based fault-tolerant control for flexible joint robotic manipulators: An actor-critic framework with

Hejia Gao¹, Chuanfeng He¹, Tanyu Chen¹

¹School of Artificial Intelligence, Anhui University, Hefei 230601, China; Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, Anhui, China; Anhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University, Hefei 230601, China.

ISA Transactions

|June 1, 2026

Summary

This study introduces a novel fault-tolerant control strategy for flexible-joint robotic manipulators (FJRMs) using Lyapunov-guided actor-critic reinforcement learning (RL). The method enhances control robustness and tracking accuracy, even with actuator faults.

Keywords:

Actuator faults Adaptive control Neural networks Reinforcement learning Robotic manipulator

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Last Updated: Jun 3, 2026

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

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

Published on: October 14, 2017

Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Flexible-joint robotic manipulators (FJRMs) offer enhanced dexterity but present control challenges due to nonlinear dynamics, coupling, disturbances, and actuator faults.
Existing control methods often struggle with these complexities, particularly in fault-tolerant scenarios.

Purpose of the Study:

To develop a robust fault-tolerant control strategy for FJRMs.
To improve tracking accuracy and system stability in the presence of actuator faults and uncertainties.
To leverage reinforcement learning for adaptive control policy optimization.

Main Methods:

A Lyapunov-guided actor-critic reinforcement learning (RL) approach was implemented.
A critic network was utilized to learn the long-run cost-to-go, considering tracking error, control effort, and fault impact.
The critic's evaluation guided online actor policy improvement within a stabilizing Lyapunov control structure.

Main Results:

The proposed strategy demonstrated improved tracking accuracy and robustness on a Baxter robotic platform under faulty conditions.
Experimental results showed superior performance compared to PID and single neural-network controllers.
Semi-global uniform ultimate boundedness of all closed-loop signals was theoretically established.

Conclusions:

The Lyapunov-guided actor-critic RL strategy offers an effective solution for fault-tolerant control of FJRMs.
This approach provides enhanced adaptability and resilience against uncertainties and actuator faults.
The method represents a significant advancement over conventional control techniques for flexible robotic systems.