Field dynamic balancing for active magnetic bearings supporting rigid rotor shaft based on extended state observer

Purpose The purpose of this paper is to present an actuator fault detection method for unmanned aerial vehicles (UAVs) based on interval observer and extended state observer. Design/methodology/approach The proposed algorithm has very little model dependency. Therefore, a six-degree-of-freedom linear equation of UAVs is first established, and then, combined with actuator failure and external disturbances in flight control, a steering gear model with actuator failure (such as stuck bias and invalidation) is designed. Meanwhile, an extended state observer is designed for fault detection. Moreover, a fault detection scheme based on interval observer is designed by combining fault and disturbances. Findings The method is testified on the extended state observer and the interval observer under the failure of the steering gear and bounded disturbances. The simulation results show that the two types of fault detection schemes designed can successfully detect various types of faults and have high sensitivity. Originality/value This research paper studies the failure detection scheme of the UAVs’ actuator. The fault detection scheme in this paper has better performance on actuator faults and bounded disturbances than using regular fault detection schemes.

show abstract

“…Considering equation (10), the extended state observer (Li et al , 2021; Liu, 2021) of the system can be presented by: …”

Section: Observer Designmentioning

confidence: 99%

Fault detection based on extended state observer and interval observer for UAVs

Cao

Wang

et al. 2022

AEAT

View full text Add to dashboard Cite

show abstract

“…Yu et al [28] proposed a new adaptive proportional-integral control strategy for rotor active balancing systems during acceleration. Li et al [29] proposed a novel disturbance-observer-based field dynamic balancing strategy for active magnetic bearings (AMB) equipped machinery. e imbalance distribution of the rotor may lead to vibrations and bifurcations in rotor-active magnetic bearing systems [30,31].…”

Section: Related Workmentioning

confidence: 99%

A Novel Balancing Method for Rotor Using Unsupervised Deep Learning

Zhong

Chen

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

A novel balancing method for rotor based on unsupervised deep learning is proposed in this paper. The architecture of the proposed deep network is described. In the proposed network, compared to the supervised deep network, additional convolution layers are applied not only for the learning of the inverse mapping but also for identifying the unbalanced force without labeled data. The equivalent value and position of imbalances in two correction planes are obtained. A case study of a rotor with two discs supported by sliding bearings is conducted. Preset imbalances are balanced well by the proposed method. And, using the state values at different time intervals, no extra weight trails are needed. The results show that the proposed balancing method gives consideration to both cost and accuracy.

show abstract

“…Inoue et al (2009) designed a disturbance observer to reduce rotor vibration by taking nonlinearity, uncertainty and imbalance mass in the system as a disturbance. Liu et al (2017) and Li et al (2021) applied the extended state observer in unbalanced vibration control of magnetically suspended control moment gyro and rigid rotor, respectively. Comparing these two disturbance compensation methods: the performance of the disturbance observer relies heavily on the accurate models; the extended state observer is better at estimating low-frequency disturbance (the frequency of modal vibration caused by interface contact is typically higher than 500 Hz).…”

Section: Introductionmentioning

confidence: 99%

Optimal phase compensation for a rotor-AMB system considering interface contact

Yang

Zhou

Wang

et al. 2023

Journal of Vibration and Control

View full text Add to dashboard Cite

Rotor-Active magnetic bearing (rotor-AMB) systems offer significant advantages in power consumption and are increasingly being applied in fluid machinery. However, in such machinery, the impeller and the rotor are commonly connected by bolt fastening. Once a certain pre-tightening force is applied to the bolted connection and the interface contact between the rotor and the impeller is formed, the flexible modal vibration will be excited when the rotor is levitated. The conventional Proportional-Integral-Derivative (PID) controller is limited in this vibration suppression and it is essential to increase the damping at bending mode to inhibit vibration. However, the calculation of the optimal phase compensated is based on the rotor-AMB model considering interface contact. In this work, the effect of the interface contact is equivalent to massless spring units, and the disturbance generated by spring units is acted upon the mechatronic system in the way of positive feedback. The detailed description of the transfer function from the disturbance to rotor deformation at fourth bending mode is obtained by theoretical model and system identification. The optimal phase of the system needed to inhibit the disturbance is determined by minimizing the gain of the transfer function at fourth bending mode. A combination of PID control and phase compensation is implemented in the experiment. The results show that the optimal phase is 50° as the calculation and the optimal phase compensation control can sufficiently inhibit the modal vibration.

show abstract

Field dynamic balancing for active magnetic bearings supporting rigid rotor shaft based on extended state observer

Cited by 23 publications

References 20 publications

Fault detection based on extended state observer and interval observer for UAVs

Fault detection based on extended state observer and interval observer for UAVs

A Novel Balancing Method for Rotor Using Unsupervised Deep Learning

Optimal phase compensation for a rotor-AMB system considering interface contact

Contact Info

Product

Resources

About