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 refere...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.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61833013,61573282,61473227,and 11472222)the Natural Sciences and Engineering Research Council of Canada
文摘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.
