Active fault-tolerant tracking control of a quadrotor with model uncertainties and actuator faults

Yu-jiang ZHONG; Zhi-xiang LIU; You-min ZHANG; Wei ZHANG; Jun-yi ZUO

doi:10.1631/FITEE.1800570

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Active fault-tolerant tracking control of a quadrotor with model uncertainties and actuator faults

Special Issue on Complex System and Intelligent Control | Updated：2022-05-19

- Active fault-tolerant tracking control of a quadrotor with model uncertainties and actuator faults
- 模型不确定性和执行器故障下的四旋翼飞行器主动容错控制方法
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 1, Pages: 95-106(2019)
- Affiliations：
  
  School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
  Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal H3G1M8, Canada
- Author bio：
  
  [ "Yu-jiang ZHONG, E-mail: yujiangzhong@hotmail.com" ]
  You-min ZHANG, E-mail: ymzhang@encs.concordia.ca
- Funds：
- DOI：10.1631/FITEE.1800570
  CLC： TP273
- Received：15 September 2018，
  
  Revised：2019-;1-;8，
  
  Published：2019-01
- Accepted：
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Yu-jiang ZHONG, Zhi-xiang LIU, You-min ZHANG, et al. Active fault-tolerant tracking control of a quadrotor with model uncertainties and actuator faults[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(1): 95-106.
DOI：

Yu-jiang ZHONG, Zhi-xiang LIU, You-min ZHANG, et al. Active fault-tolerant tracking control of a quadrotor with model uncertainties and actuator faults[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(1): 95-106. DOI： 10.1631/FITEE.1800570.

摘要

针对四旋翼飞行器的执行器故障，提出一种可靠的主动容错控制方法。该方法以模型参考自适应控制理论为框架，保证四旋翼飞行器系统的全局渐进稳定性。为消除模型不确定性影响，增强系统鲁棒性，径向基神经网络算法被集成到所设计的控制系统中，自适应地辨识模型不确定性，在线调整参考模型。此外，为避免因执行器饱和及响应速率限制造成的不必要的系统性能下降，在控制器设计过程中，同时考虑执行器动态特性。基于自适应两级卡尔曼滤波器设计的故障检测与诊断模块，可以准确估计执行器控制效率损失故障。利用获取的故障信息，重新构造控制器的控制律，弥补执行器故障的不利影响。仿真结果表明，在执行器有、无故障两种情况下，提出的主动容错控制方法都能使四旋翼飞行器准确跟踪期望的参考信号。

Abstract

This paper presents a reliable active fault-tolerant tracking control system (AFTTCS) for actuator faults in a quadrotor unmanned aerial vehicle (QUAV). The proposed AFTTCS is designed based on a well-known model reference adaptive control (MRAC) framework that guarantees the global asymptotic stability of a QUAV system. To mitigate the negative impacts of model uncertainties and enhance system robustness

a radial basis function neural network is incorporated into the MRAC scheme for adaptively identifying the model uncertainties online and modifying the reference model. Meanwhile

actuator dynamics are considered to avoid undesirable performance degradation. Furthermore

a fault detection and diagnosis estimator is constructed to diagnose lossof-control-effectiveness faults in actuators. Based on the fault information

a fault compensation term is added to the control law to compensate for the adverse effects of actuator faults. Simulation results show that the proposed AFTTCS enables the QUAV to track the desired reference commands in the absence/presence of actuator faults with satisfactory performance.

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references

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