Hydraulic active suspension has the coupling characteristics of integrated mechanical-hydraulic-control system, and its performance is also affected by external interference, noise interference, and other factors. Therefore, a novel controller is presented to achieve system-level optimal performances by using the collaborative control method. The proposed collaborative method is provided an implementation framework, and includes outer loop control of Linear Quadratic Regulator (LQR), inside loop control of modified linearized active disturbance rejection control (M-LADRC) and Kalman filter, and so on. In addition, the semi-physical simulation method is used to the simulation and test, and the force tracking control test for hydraulic servo system and Kalman filter test are carried out. Finally, the performance of semi-physical active suspension is compared with that of the ideal active suspension and passive suspension under the different road profile excitation. The superiority and effectiveness are verified for the collaborative control method. The collaborative controller has improved the suspension system performance and is more practical, which is beneficial for the application of the achievements.

The condition monitoring and fault diagnosis have been identified as the key to achieving higher availabilities of wind turbines. Numerous studies show that the open-circuit fault is a significant contributor to the failures of wind turbine converter. However, the multiple faults combinations and the influence of wind speed changes abruptly, grid voltage sags and noise interference have brought great challenges to fault diagnosis. Accordingly, concerning the open-circuit fault of converters in direct-driven PMSG wind turbine, a diagnostic method for multiple open-circuit faults is proposed in this paper, which is divided into two tasks: The first one is the fault detection and the second one is the fault localization. The detection method is based on the relative current residuals after exponential transformation and on an adaptive threshold, and the localization method is based on the average values of fault phase currents. The scheduled diagnosis method is available to both the generator-side converter and the grid-side converter, allowing to detect and locate single and double open-circuit faults. For validating this, robustness test and multiple open-circuit faults diagnosis are presented in a 2-MW direct-driven PMSG wind turbine system, the results validate the reliability and effectiveness of the proposed method.

The dynamic parameter allocation of the suspension system has an important influence on the comprehensive driving performance of the tracked vehicle. Usually, the allocation of suspension parameters is based on a single performance index, which has the disadvantage of not being able to achieve multi‐performance optimization. Therefore, a novel optimization method using multi‐performance index‐oriented is presented. Firstly, considering the vertical vibration excitation caused by road roughness, the input (excitation) model of road roughness is embedded to establish the parametric dynamic model of the tracked vehicle. Then, the evaluation index and its quantitative algorithm, which reflect the multi‐aspect performance of the suspension system, are proposed. Moreover, the parameter allocation objective function based on multi‐index information fusion is designed. Finally, two allocation optimization methods are presented to solve the parameter allocation, i.e., equal weight allocation and expert knowledge‐based weight allocation. By comparing the results obtained by the two methods, it is found that the performance of the suspension system can be improved effectively by optimizing the parameters of suspension stiffness and damping. Furthermore, the optimization of weight allocation based on expert knowledge is more effective. These provide a better knowledge reference for suspension system design.

The inverter is one of the key components of wind turbine, and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices. Therefore, it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit, especially in the early stages of failure. However, if the inverter temperature prediction model can be established, the early states can be identified through the judgment of the output temperature. Accordingly, considering whether the inverter heats up normally from the perspective of heat dissipation, a method for the early operation state identification of the inverter is provided in this paper. A variable selection method based on fusion analysis of correlation and physical relationship is adopted to extract variables as input variables, which have high correlation with inverter temperature. Then multi-input and multi-output temperature prediction model of inverter is established based on a non-linear autoregressive exogenous model (NARX) network, and the prediction temperature residual is used as the real-time standard to evaluate the inverter states. For validating this, the validity and reliability of the established temperature prediction model are verified through case analysis, and the performance comparison with various models demonstrates that the proposed method has higher accuracy. The construction method of the prediction model can be used for reference to other aspects of wind turbine. All these bring huge benefits to wind energy industry.

High-precision estimation of road elevation is essential for ensuring ride comfort and driving safety. However, the complexity of real-world road scenes and the influence of external factors on sensor measurements hinder existing methods from achieving both high accuracy and computational efficiency. To tackle this challenge, this paper presents an efficient multimodal Bird’s-eye view (BEV) framework for road elevation estimation, termed RE ² Mamba, which integrates LiDAR geometric data with RGB appearance information. Rather than engaging in costly global reasoning on dense BEV grids, the proposed method performs global multimodal context modelling within a compact representation using a Mamba state-space model, generating high-resolution BEV outputs through geometrically consistent alignment and BEV backfilling. To further improve cross-modal complementarity and long-range consistency, this paper introduce dual-path feature propagation for LiDAR and image tokens, while broadcasting global context to each BEV cell. The network predicts a discretised elevation distribution and decodes continuous elevations by taking expectations over bin centres. On the RSRD dataset, with a BEV resolution of 0.03 m, RE ² Mamba achieves a mean absolute error of 0.57 cm and a root mean square error of 0.73 cm, with an end-to-end latency of 10 ms per frame. Ablation studies further substantiate the effectiveness of token-domain global modelling, as well as BEV backfilling and broadcasting. These results illustrate a favourable accuracy-latency trade-off and highlight the potential of the proposed framework for resource-constrained real-time applications.