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This paper proposes a neural network-interacted robust coordinated control (NNIRCC) scheme to address the problem of full-vehicle active air suspension (AAS) systems subject to uncertainties and different time-varying actuator delays. The NNIRCC scheme consists of neural network-interacted (NNI) approximator, projector-based estimator and robust coordinated control term. The NNI approximator based on the radial basis function is employed to capture the nonlinearities caused by the unmodeled dynamics of adjustable air spring. Meanwhile, an interactive updating algorithm is designed to manipulate the weights of the NNI approximator so as to improve the approximation accuracy. Moreover, projector-based nonlinear estimators are designed to handle the prevalent sensitive parameter variations (such as vehicle body mass and its moments of inertia). Furthermore, delay compensators are developed and integrated into the synthesized coordinated control law to mitigate the impact of different time-varying input delays caused by force actuators. The asymptotic stability of closed-loop system is rigorously proven by employing a Lyapunov-Krasovskii functional, guaranteeing the boundedness of both tracking and estimation errors within a finite time. Additionally, co-simulation results are provided and analyzed, illustrating the feasibility and efficiency of the proposed NNIRCC scheme.

Model predictive control (MPC) has been commonly recognized as a promising control strategy for the quasi Z-source inverter (qZSI). However, large control error is regarded as an innate drawback of MPC due to only one voltage vector applied per control cycle. In this paper, an improved MPC, namely combinative voltage vectors based MPC, is proposed for the qZSI to reduce the control error and improve steady-state performance. Two different voltage vectors are applied in one control cycle, so that the cost function value can be reduced. The errors of the inductor current, the capacitor voltage, and the output current of the qZSI included in the cost function can be decreased greatly. Furthermore, the performance investigation of the proposed method indicates that the two selected voltage vectors consisting of a non-shoot-through vector and a shoot-through vector is of help in shorting the charge-discharge period of inductor. Thus, the inductor current ripple as well as the total harmonics distortion (THD) of the output current is greatly reduced. The experimental results show the validity and the advantages of the proposed method.

Accurate estimation of orientation is very important in the field of inertial navigation. With the development of the technology of micro electro mechanical systems (MEMS), suppressing the influence of a gyro drift by means of continuous rotation (“carouseling”) has become a research hotspot. However, most of the current studies are still experiments conducted on the horizontal plane but carrying out north-seeking research on non-horizontal planes is also much valuable because it is common in practical scenarios, such as in the mining and drilling environment. In this paper, a feasible dynamic north-finder method for a micro inertial measurement unit (MIMU) based on the MEMS technology on a tilting plane is proposed. The fast Fourier transform (FFT) algorithm is used to analyze the appropriate rotation rate, and the tilting angle and heading angle are calculated in real time by an optimal weighted fusion based on recursive least squares (OWFBRLS) algorithm. Higher-precision orientation results can be achieved through the introduction of the optimal weighted fusion (OWF) theory. This paper demonstrates the experimental method and data processing in detail. The experimental results indicate that the proposed algorithm can provide accurate inclinations and headings, and the standard deviation (SD) of heading angle can reach approximately 0.6° in 3 minutes, which is superior to the methods in the literature and still has high precision in the case of large tilting angles. These profound results prove the feasibility and effectiveness of the proposed method.