To achieve the high-accuracy and high-bandwidth attitude-rate measurement and control integration of spacecraft, a novel method based on a magnetically suspended control and sensitive gyroscope (MSCSG) configuration is proposed in this paper. MSCSGs are employed as both actuators and attitude-rate sensors. First, the structure and principle of a MSCSG is given and its modeling is established. Second, the attitude-rate measurement method based on a four-cross configuration is proposed. The corresponding attitude decoupling control law of spacecraft and the steering law of the MSCSGs have been further designed. Compared with the traditional attitude-rate measurement methods, the proposed one entirely preserves the inertial coupling moment items and cross-coupling moment items in the resolution of attitude rate by making full use of the dynamic information of the configuration. Thus, the measurement precision can be greatly improved, and the traditional tension between the accuracy and bandwidth can be well alleviated. The proposed method also enlarges greatly the attitude control bandwidth by the microgimbal moment, providing a new method for high-frequency vibration suppression. Semiphysical simulation results demonstrate the correctness and superiority of the proposed method.

Rotor unbalance (RUB) and Sensor Runout (SR) are two main vibration sources for active magnetic bearing (AMB) system, and they not only produce considerable harmonic vibration but also endanger the stability of AMB system. Therefore, a nonlinear adaptive algorithm, whose asymptotic stability is guaranteed by Lyapunov's theory, is proposed in this paper to suppress the harmonic vibration. Nevertheless, RUB and the synchronous component of SR have the same frequency with the rotational frequency, and it is highly necessary to distinguish them from each other for the compensation of displacement stiffness force. Hence, the scheme of changing rotor speed is adopted for the permanent magnet biased magnetic bearing system. As a consequence, the harmonic current caused by RUB and SR is effectively attenuated and the displacement stiffness force is correctly compensated. The results of the numerical simulations and experiments demonstrate that the harmonic vibration is dramatically suppressed through the proposed method.

The harmonic current in active magnetic bearing (AMB) system usually results from rotor unbalance and sensor runout. It not only causes considerable harmonic vibration, but also brings about the power saturation and the instability of AMB system especially for the high-speed rotor. Therefore, a novel parallel finite dimension repetitive controller (PFDRC) is proposed to restrain the harmonic current in this paper. Compared with the conventional repetitive controller (RC), PFDRC only introduces the limited imaginary poles, so that the problem that the infinite imaginary poles of RC endanger the stability and instantaneous characteristic of the closed-loop system is solved. In order to realize the harmonic current suppression, the power amplifier system is taken as the controlled object, and the proposed PFDRC is parallel with the original equivalent main controller. Then the root reclus method is adopted to design the parameters and to analyze the closed-loop system stability. Based on the phase-frequency characteristic of the system function, the compensators are introduced to improve the system stability margin. Meanwhile, the low-pass property of power amplifier system is sufficiently taken into consideration in the parameters design. Finally, the simulations and experiments are implemented to validate the effectiveness of the proposed PFDRC, and the results demonstrate that the harmonic current is dramatically restrained by the proposed method.

Switching power amplifier is an essential component for magnetic bearing system. However, the current ripple resulted from the conduction and shutoff operation of power switch component inevitably affects the performance of active magnetic bearing. To reduce the current ripple of switching power amplifier, the characteristics of current ripple are first analysed. In response to the spectrum character of current ripple, an online lifting wavelet transform de‐noising algorithm, unlike the conventional power filter circuit, is proposed in this study. Moreover, the sliding data window and an improved threshold function are introduced to realise the feasibility of online processing and to improve the de‐noising performance, respectively. Finally, plentiful comparative experiments, taking the magnetically suspended flywheel as subject, are implemented. The results illustrate that the proposed algorithm could dramatically reduce the current ripple and that the rotor displacement jitter and the vibration force are suppressed accordingly.

As a critical component of aircraft flight control systems, aileron actuator’s fault directly affects the performance of flight control system and the overall flight safety of the aircraft. Therefore, effective fault diagnosis of aileron actuators becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators is more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on parallel-symmetrical dot pattern (SDP) and polar sparse representation. First, the current residual signals of force motors are obtained using a bi-step observer. Then, the residual signals are transformed into parallel-SDP images, where each of the four petals of the parallel-SDP image is generated from the corresponding channel. The parallel-SDP image presents global fault information and captures subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of the parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deep-learning-based method, which is suitable for embedded computing system in aircraft.