Work

Learning estimator‐based fault‐tolerant control for robot manipulators with partial loss of actuator effectiveness

Year: 2025

Type: article

Abstract: This paper proposes a fault‐tolerant control approach for robot manipulators, addressing challenges such as parameter uncertainties, external disturbances, control input limitations, and partial loss of actuator effectiveness. As an essential step, an improved learning estimator is designed to identify actuator faults by introducing a sign function. Based on the estimated fault information, a fault‐tolerant control law is subsequently developed. To address the challenge of control input saturation, a saturation compensation mechanism is integrated into the control law. Finally, numerical simulations are performed to verify the performance and efficacy of the proposed learning estimator and control strategy, confirming its feasibility and robustness under various fault conditions.

Source: Asian Journal of Control

DOI: 10.1002/asjc.3594

Authors: Jianbang Huang, Teng Cao, Zhe Zhang, Shaohua Yang

Institutions: Qufu Normal University, Nanyang Technological University, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, National Natural Science Foundation of China

Award: G4007053373