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Adaptive performance enhancement control for flexible-joint manipulator with model uncertainties and actuator
Hejia Gao1, Yuanyuan Zhao1, Chuanfeng He1
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
|December 24, 2025
Summary
This study introduces an adaptive performance enhancement (APE) control method for flexible-joint robotic manipulators (FJRM). It improves trajectory tracking and robustness against uncertainties and failures, outperforming existing methods.
Area of Science:
- Robotics
- Control Systems Engineering
- Artificial Intelligence
Background:
- Flexible-joint robotic manipulators (FJRM) offer high flexibility and precision but are prone to malfunctions.
- These malfunctions degrade operational stability, accuracy, and equipment lifespan.
- Effective control strategies are crucial for reliable FJRM operation.
Purpose of the Study:
- To propose a novel adaptive performance enhancement (APE) control method for FJRM.
- To address model uncertainties and actuator failures in FJRM systems.
- To enhance trajectory tracking accuracy and system robustness.
Main Methods:
- An adaptive neural network (ANN) algorithm compensates for modeling errors.
- A non-singular terminal sliding mode (NTSM) policy provides compliance control.
- Lyapunov's direct method validates closed-loop system stability.
Main Results:
- The APE control method demonstrated effective trajectory tracking for uncertain robotic systems.
- NTSM control enhanced robustness and interference suppression.
- Simulations and experiments on the Gazebo platform and Baxter robot confirmed effectiveness.
Conclusions:
- The proposed APE control method significantly outperforms FNN, NN, and PD controllers.
- The APE method offers superior control performance for FJRM.
- This research contributes to more stable and reliable robotic manipulator operations.
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