Regular Papers

International Journal of Control, Automation and Systems 2021; 19(12): 4010-4024

Published online December 6, 2021

https://doi.org/10.1007/s12555-020-0643-y

© The International Journal of Control, Automation, and Systems

Characteristic Model-based Adaptive Fault Tolerant Control for Four-motor Synchronization Systems Considering Actuator Failure

Yang Gao, Jiali Ma, Qingwei Chen, and Yifei Wu*

NanJing University of Science and Technology

Abstract

This article proposes a characteristic model-based adaptive fault tolerant control scheme to deal with actuator failure in four-motor synchronization systems, which usually causes sudden inertia ratio change and backlash increase. Firstly, the characteristic modeling method is applied into servo system to obtain a simplified system model without losing high-order features. Also, this model could reflect real-time system status through three characteristic parameters. Secondly, a particle swarm optimization algorithm-based estimator is designed to identify characteristic parameters online. By this way, the characteristic model could react to inertia ratio changes quickly and eliminate its negative effect in signal tracking. Thirdly, an improved adaptive electric anti-backlash method is used to restrain backlash. Compared to regular anti-backlash technique, this adaptive one uses a neural networkbased fault detector to monitor motors and adjust bias current according to different actuator status, even when one motor breaks down. With these three steps combined, a fast terminal sliding mode controller is finally designed as fault tolerant controller and the stability of this closed-loop system is guaranteed by Lyapunov stability theorem. At last, the simulation and experiment results prove the effectiveness of the proposed control scheme in system control and fault tolerance.

Keywords Adaptive electric anti-backlash, characteristic model, fast terminal sliding mode control, four-motor synchronization system, particle swarm optimization.

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(12): 4010-4024

Published online December 1, 2021 https://doi.org/10.1007/s12555-020-0643-y

Copyright © The International Journal of Control, Automation, and Systems.

Characteristic Model-based Adaptive Fault Tolerant Control for Four-motor Synchronization Systems Considering Actuator Failure

Yang Gao, Jiali Ma, Qingwei Chen, and Yifei Wu*

NanJing University of Science and Technology

Abstract

This article proposes a characteristic model-based adaptive fault tolerant control scheme to deal with actuator failure in four-motor synchronization systems, which usually causes sudden inertia ratio change and backlash increase. Firstly, the characteristic modeling method is applied into servo system to obtain a simplified system model without losing high-order features. Also, this model could reflect real-time system status through three characteristic parameters. Secondly, a particle swarm optimization algorithm-based estimator is designed to identify characteristic parameters online. By this way, the characteristic model could react to inertia ratio changes quickly and eliminate its negative effect in signal tracking. Thirdly, an improved adaptive electric anti-backlash method is used to restrain backlash. Compared to regular anti-backlash technique, this adaptive one uses a neural networkbased fault detector to monitor motors and adjust bias current according to different actuator status, even when one motor breaks down. With these three steps combined, a fast terminal sliding mode controller is finally designed as fault tolerant controller and the stability of this closed-loop system is guaranteed by Lyapunov stability theorem. At last, the simulation and experiment results prove the effectiveness of the proposed control scheme in system control and fault tolerance.

Keywords: Adaptive electric anti-backlash, characteristic model, fast terminal sliding mode control, four-motor synchronization system, particle swarm optimization.

IJCAS
June 2024

Vol. 22, No. 6, pp. 1761~2054

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