To optimize the voltage regulation performance and enhance the robustness, an adaptive model predictive controller is proposed in this article for an interleaved dc–dc boost converter. The predictive model is constructed by linearizing the nonlinear equations of the converter at the current operating point. A novel parameter update mechanism is developed based on the static model, which enables fast and accurate identification of the model parameters, thus the control law can guarantee the desired control performance in the wide operating range. Based on the adaptive model, a disturbance observer is constructed to correct the model predictions and mitigate parameter uncertainties. Then an explicit control law is derived by solving the optimization problem offline, and the stability analysis is carried out using the linear control theory. Finally, the effectiveness of the proposed method is verified by both simulation and experimental results.

The increasing environmental issues such as air pollution and energy scarcity of fossil fuels require the acceleration of the electrification in various fields, especially for the transportation. According to this development trend, fuel cell systems gradually become a potential alternative to traditional power systems for high efficiency, high energy density and zero pollution, while the relatively short life time has tremendously limited its large-scale commercial application. To extend the fuel cell lifespan, degradation predictive methods are proved to have outstanding effect. In this article, a hybrid prediction model based on deep temporal feature extraction and transfer learning is proposed. Firstly, the health index is constructed by extracting the fuel cell degradation features through a temporal convolutional network; on this basis, transfer learning is performed according to the feature extraction, and finally the extracted features are input into the long short-term memory model to complete the fuel cell degradation prediction. The effectiveness of the algorithm is verified with experimentally extracting degradation data under different operating conditions, and the results show that the proposed method can effectively improve the prediction accuracy and decrease the computation by extracting key parameters of fuel cell degradation and is independent of operating conditions.

In this paper an adaptive non-singular fast terminal sliding mode (NFTSM) control scheme is proposed to control the electro-mechanical actuator (EMA) in an electric braking system which is a complex electro-mechanical system. In order to realize high-performance brake pressure servo control, a radial basis function (RBF) neural network method is adopted to deal with the difficulty of estimating the upper bound of the compound disturbance in the system, to reduce the conservatism of the design of sliding mode switching gain, and effectively eliminate sliding mode chattering. The simulation results show that, compared with a linear controller, the proposed control strategy is able to improve the servo performance and control precision. In addition the response speed of the braking actuator is enhanced significantly, without changing the traditional double-loop control structure.

Both foil structure and surface texturing have been widely used to improve bearing performance. However, there is little research on their combination, namely, textured gas foil bearing. This paper adopts the Reynolds equation as the pressure governing equation of bump-type foil journal bearing to study the influence of textures located on the top foil. The Newton-Raphson iterative method and the perturbation method are employed to obtain static and dynamic characteristics, respectively. Thereafter, based on three texture distribution types, further analysis about the effect of the relative texture depth and the textured portion is carried out. The results indicate that an appropriate arrangement of textures could improve the performance of gas foil bearing. For #1 texture distribution, the maximum increment of load capacity could exceed 10% when ω = 1.4 × 10⁵ r/min, ε = 0.2.

The power converter plays a crucial role in energy conversion systems, while advanced control algorithms can help stabilize the bus voltage and match the power demand. In this work, a novel fast terminal sliding mode control is proposed for boost converters to strengthen the disturbance rejection ability. First, a full-order sliding mode manifold is proposed to guarantee the finite-time convergence of system states, and an integral-type switching control law is proposed to deal with large disturbances. Second, the disturbance estimation method is used to identify the system uncertainties and theoretical analysis indicates that it can further enhance the robustness and reduce the chattering. Third, a parameter update mechanism is developed to calculate the model parameters at the current operating point, which contributes to improving the control performance and flexibility to different working conditions. Finally, simulation and experimental tests are conducted to verify the proposed method.