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.

Fuel cell systems are emerging as a strong alternative to traditional power sources due to their high energy efficiency, greater energy density, and zero emissions. However, their relatively short lifespan has limited their widespread commercial use. Predictive methods for estimating the state of health (SOH) and remaining useful life (RUL) have been effective in extending fuel cell life. Traditional diagnostic methods, such as electrochemical impedance spectroscopy, require system shutdowns, disrupting operations. To solve this problem, a nonintrusive hybrid machine learning model combining extreme gradient boosting (XGB) and random forest (RF) is proposed. This hybrid method addresses two main issues: improving prediction accuracy and making the model more reliable. The novelty lies in combining the strengths of both algorithms, achieving better prediction accuracy and reducing errors compared to single-model methods. It provides continuous and scalable SOH and RUL predictions, helping improve fuel cell durability without interrupting operations. The model is tested on a fuel cell aging dataset, which includes data from static and dynamic conditions, and is validated by yielding root mean squared errors (RMSEs) of 0.00026 and 0.00127 for SOH and RUL predictions, respectively. Compared to standalone XGB and RF models, the hybrid approach demonstrates superior prognostic performance.

The single-bus dc electrical power system is a promising architecture for more electric aircraft. However, the high penetration of the constant power loads (CPLs) will seriously threaten the system stability. To this end, in this article, a novel model predictive control (MPC) is proposed to eliminate the destabilizing effects of CPLs and optimize the transient performance of the electrical power systems over a wide operating range. First, a state coordinate system is introduced to obtain a canonical form model, and the newly defined output state is disturbed by load power and system uncertainties. Then, the finite-time disturbance observers are utilized to estimate the dynamic load power and system uncertainties. Second, the estimated values are integrated into MPC to regulate the output state to its reference value. By solving the optimization problem offline, a continuous-time MPC scheme is obtained explicitly. Besides, different from the existing MPC schemes that make system states converge to equilibrium points in infinite time, the concept of reference trajectory in the discrete-time MPC schemes is introduced into the continuous-time MPC framework for the first time, and a new second-order reference trajectory with terminal attractor is proposed, yields a finite-time MPC law. Finally, the effectiveness of the proposed MPC scheme is verified by experimental tests.

Owing to peak power demands of pulsed power load like radar and beam weapon is much larger than capability of generator, researches about energy storage equipment sizing optimization have been extensively carried out. However, these researches are mainly considered from static perspective without taking dynamic characteristic into consideration. To cover this gap and meet strict weight requirements of airborne platform, this paper proposed a hybrid energy storage system sizing process to satisfy peak power demand while dynamic standard set up by MIL-STD-704F is also studied by means of spectrum and impedance analysis. The overall filter capacitor on DC BUS is considered as a part of energy storage system for improving system dynamic characteristics. The research content is divided into four parts. First, the power waveform is discussed through spectrum analysis to give full play to hybrid energy storage system. Second, the influence of energy storage equipment on system dynamic characteristics is analyzed and the results are taken as constraints for optimization. Then, combined with dynamic and static constraints, a hybrid energy storage system sizing process depend on Non-dominated Sorting Genetic Algorithm II and decision-making method are proposed to obtain the most suitable result. Finally, simulation model is established in MATLAB/Simulink environment for verification.

The moving average filter-based phase-locked loop (MAF-PLL) can obtain grid synchronization signals accurately under adverse grid conditions with a large amount of harmonics due to the high filtering capability of the MAF. However, MAF-PLL cannot achieve a fast dynamic response in the case of frequency drift, phase angle steps, and unbalanced voltage sag. MAF is essentially an FIR filter, and its filtering performance is hard to be adjusted. To address this issue, this paper proposes an alternative to MAF consisting of a set of cascading second-order IIR filters (CIIRF). Based on MAF, CIIRF introduces multiple zeros and poles from the zero–pole replacement perspective, and by changing the position of the poles, the filter performance can be adjusted. To improve the anti-interference ability of PLL based on CIIRF (CIIRF-PLL) in the presence of grid frequency drift, a frequency-adaptive scheme is also proposed. Simulation and experimental results show that CIIRF-PLL can accurately track the grid voltage phase in the case of frequency steps, phase angle jumps, harmonics injection, and unbalanced voltage sag and has good steady-state and dynamic performance.