Robustness Enhancement Strategy for Flexible Joints Based on Model Predictive Current and Adaptive Control

To improve the response speed, working accuracy and anti-interference ability of collaborative robots, a collaborative robot control method based on model prediction and adaptive algorithm is proposed. A non-parametric model predictive current control method is employed to update current data in rea...

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Vydáno v:Journal of electrical engineering & technology Ročník 20; číslo 8; s. 5177 - 5188
Hlavní autoři: Yang, Yifei, Zhao, Jianxing, Sun, Xiaodong, Wang, Yong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.11.2025
Springer Nature B.V
대한전기학회
Témata:
Accuracy
Adaptive algorithms
Adaptive control
Algorithms
Angular acceleration
Collaboration
Control algorithms
Control methods
Dynamic response
Electrical Engineering
Electrical Machines and Networks
Electronics and Microelectronics
Engineering
Flexibility
Friction
Instrumentation
Original Article
Parameter estimation
Parameter identification
Power Electronics
Predictive control
Robot control
Robots
Robustness
Sensors
Systems stability
전기공학
Flexible joints
Robust control
Model predictive current control (MPCC)
Adaptive control
ISSN:1975-0102, 2093-7423
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Shrnutí:To improve the response speed, working accuracy and anti-interference ability of collaborative robots, a collaborative robot control method based on model prediction and adaptive algorithm is proposed. A non-parametric model predictive current control method is employed to update current data in real time. It can effectively reduce the impact of parameter variations on control performance. Meanwhile, at the linkage end, a nonmodel-based adaptive control is adopted and combined with the recursive least squares (RLS) algorithm for online dynamic parameter estimation. This method eliminates the need for extra sensors and thus reducing costs. By integrating alternative system functions with a nonmodel-based adaptive algorithm to estimate their outputs, this method diverges from traditional estimation techniques based on the original dynamics model, thereby circumventing the need for measuring angular acceleration and integration operations and avoiding error accumulation. Experimental results demonstrate that the proposed method not only enhances the system’s dynamic response speed and tracking accuracy but also significantly improves its robustness in the presence of complex external disturbances.
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ISSN:1975-0102
2093-7423
DOI:10.1007/s42835-025-02460-7