Zuo Wang

The sliding mode controller (SMC) is inherently sensitive to noise due to its discontinuous control, especially under mismatched disturbances. To improve the noise robustness and disturbance rejection of SMC, this work proposes a tightly integrated framework for DC–DC converters. Rather than simple combinations of separate modules, it seamlessly unifies a filter, disturbance observer (DOB), and SMC. A novel discrete-time interconnected observer, fusing a DOB and an adaptive Kalman filter, simultaneously reconstructs disturbances and suppresses noise. By leveraging filtered states and disturbance estimates, compensation for both mismatched disturbance and input discretization dynamics is explicitly embedded into an improved sliding mode surface. Rigorous stability analysis of the closed-loop system is provided, and comparative simulations and experiments on DC–DC converter are conducted against three benchmark control methods.

This paper proposes a novel predictive observer‐based control (PO‐BC) scheme to address the speed regulation problem of a DC‐DC converter‐fed DC motor, which takes full account of system delays and disturbances. Unlike conventional cascaded control structures, the proposed approach employs a holistic fourth‐order model that explicitly accounts for the cumulative time delays inherent in the system. To counteract the destabilizing effects of these delays, a specialized predictive observer is developed to concurrently reconstruct system states and lumped disturbances. Based on the predicted information, a composite control law is synthesized to achieve active delay compensation and robust disturbance rejection. A significant advantage of the proposed scheme is its streamlined, noncascaded architecture, which requires only partial system states while maintaining high regulation accuracy. Rigorous theoretical analysis is conducted to demonstrate the stability of the closed‐loop system. Finally, comprehensive experimental results are presented to validate the effectiveness and superiority of the proposed PO‐BC scheme.

Online parameter identification is important for sensorless permanent magnet synchronous motor (PMSM) drives because motor parameter variation can reduce the accuracy of the controller and observer. However, in the background of sensorless control, the accuracy of online parameter identification is significantly affected by rotor position estimation errors and inverter nonlinearity. To address these problems, this paper proposes a high-frequency d-axis voltage injection-based online parameter identification method with inverter nonlinearity compensation. The proposed online identification method can identify the stator resistance and d-axis inductance independently. It not only overcomes the rank-deficiency problem in conventional voltage-equation-based identification, but also shows through theoretical analysis that the identification results are insensitive to rotor position estimation errors. To improve the identification accuracy, the influence and importance of inverter nonlinearity on parameter identification are analyzed, and a compensation method based on zero-sequence voltage characteristics and a feedforward neural network is developed. The identified voltage error is compensated through equivalent dead-time correction. Simulation and experimental results verify the advantages of the proposed method under different operating conditions.

As critical components in transmission systems, planetary gearboxes require precise intelligent fault diagnosis to ensure optimal performance. Most existing methods rely solely on individual vibration signals for training, a practice that leads to the dual problems of insufficient data diversity and over-parameterized models. This paper presents a novel framework that integrates multi-modal sensor fusion with a lightweight multi-head attention mechanism to effectively address these challenges. Firstly, to enhance discriminative capability, multi-axis vibration measurements (x and y-direction) from the planetary gearbox are fused with encoder data to capture complementary dynamic characteristics. Then, a hierarchical cross-modal fusion method with a gating mechanism is introduced to enhance feature reliability and richness while reducing dimensionality. Within the proposed hierarchical architecture, we first perform intra-vibration feature consolidation to remove spectral redundancy, followed by cross-domain fusion with encoder signals to amplify discriminative characteristics. Finally, to reduce computational complexity, redundant information is reduced by applying average pooling to the multi-head attention inputs. Additionally, the value vector in the conventional attention mechanism is replaced with the input itself to decrease the number of parameters. Extensive experimental validation under varying operating conditions demonstrates that the proposed method simultaneously enhances fault diagnosis accuracy and significantly reduces computational demands.

Combination gas wall-hung boiler systems have been serving as indispensable fundamental components in both industrial and domestic applications, which play significant roles in energy efficiency improvement and carbon emission reduction. However, the challenges are imposed to high precision temperature control for the systems, including time-delay, parameter uncertainties, as well as the temperature fluctuations of inlet water. In this paper, the temperature regulation problem is addressed by applying time-delay disturbance observer based composite control algorithm. First, a novel time-delay disturbance observer is constructed to have accurate estimations of the lumped disturbances. Then, feedback linearization and linear state feedback controller are combined with the proposed disturbance observer, which results in a composite control solution and improves the disturbance rejection ability. Faster dynamic responses and higher tracking accuracy are obtained. Rigorous stability analysis of the closed-loop system is presented. Finally, both simulation and experimental results are illustrated to demonstrate the effectiveness of the proposed method.