To improve the dynamic characteristics of an electro-hydraulic valve system with an independent double-valve core at the load port, this study derives the system state space equation of the main stage closed-loop system, including the structural characteristics of a pressure-reducing valve, a relief valve, a system pipeline, and a cavity based on the power bond diagram theory. The influence of the listed structural parameters on the dynamic characteristics of the main valve’s position is analyzed by the first-order sensitivity method. Since the pilot stage structural parameters of a feedback control system are crucial to the main valve’s motion characteristics, the influence of a different pilot valve’s port structural characteristics on the electro-hydraulic valve system’s characteristics is also studied. The matching performance, linearity, and control sensitivity are used as optimization objectives, the no-self-excited oscillation is set as a constraint condition, and a pilot valve’s structural parameters are self-optimized. The representative structural parameters are used for sensitivity analysis and test verification. Based on the PIV test, similar tests are conducted for different pilot valve port forms, and the pilot valve’s port structural parameters are tested both before and after parameter optimization. The theoretical and experimental results show that the factors that have a significant influence on the main valve's fretting characteristics include the closed-loop proportional gain, the main valve core friction, the spring preload force, and the liquid capacity of the non-spring control chamber. When the pilot valve’s port is U-shaped, the linearity of the flow gain, control sensitivity, and the fretting characteristics of the main valve are significantly improved.
Obtaining accurate motion state information is the foundation of active movement control systems and advanced driving assistance system. To accurately obtain vehicle motion state information without the high-accuracy dynamic models parameters, this paper proposes a vehicle state estimation algorithm based on Whale Optimization Algorithm-Support Vector Regression (WOA-SVR). A double-layer (longitudinal and lateral) regression estimation structure has been proposed based on analyzing the characteristic of vehicle dynamics. Then, the SVR model is trained on a dataset composed of multiple driving conditions, and the WOA is used to optimize the penalty factor c and kernel function parameter g in the relaxation variable during the training process; Finally, the estimation algorithm was validated through virtual simulation of single line change and frequency sweep tests, as well as ABS braking and double line change actual vehicle tests. The results show that this algorithm effectively improves estimation accuracy and is robust to changes in speed.
Purpose This paper aims to study the tribological characteristics of the electrical contact system under different displacement amplitudes. Design/methodology/approach First, the risk frequency of real nuclear safety distributed control system (DCS) equipment is evaluated. Subsequently, a reciprocating friction test device which is characterized by a ball-on-flat configuration is established, and a series of current-carrying tribological tests are carried out at this risk frequency. Findings At risk frequency and larger displacement amplitude, the friction coefficient visibly rises. The reliability of the electrical contact system declines as amplitude increases. The wear morphology analysis shows that the wear rate increases significantly and the degree of interface wear intensifies at a larger amplitude. The wear area occupied by the third body layer increases sharply, and the appearance of plateaus on the surface leads to the increase of friction coefficient and contact resistance. EDS analysis suggests that oxygen elements progressively arise in the third layer as a result of increased air exposure brought on by larger displacement amplitude. Originality/value Results are significant for recognizing the tribological properties of electrical connectors in nuclear power control systems. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0098/
System uncertainty, external disturbances and complex nonlinearities are the main obstacles for high precision control of HESVs. Due to good anti-disturbance capability and low model dependence, linear active disturbance rejection control (LADRC) is popular in engineering practice. However, when LADRC is applied to HESVs, LADRC has limited anti-disturbance capability for high-pressure gas flow force, slow response speed and small response bandwidth, and quickly causes step oscillations. In this paper, the reasons for the remarkable phenomenon of step oscillations and the limited anti-disturbance capability of LADRC are theoretically analyzed and then proved by simulations and experiments. Subsequently, a new robust anti-disturbance control method (RI-ESO) is proposed for the HESV to solve the problem of step oscillations and further improve the dynamic response and anti-disturbance capability. The RI-ESO combines an extended state observer (ESO) and a robust controller. The partial total disturbance is estimated by ESO and then compensated online. The robust integrated controller (RI) can further compensate the remaining nonlinearities and external disturbances to achieve better control performance. The introduction of ESO can reduce the parameter limitations of the robust controller. Various simulations and experiments demonstrate that the RI-ESO can greatly improve the control performance and anti-disturbance capability of HESVs, and effectively avoid step oscillations.
In response to the problems of multi-source influences,high quality feature extraction and selection,complexity and nonlinearity in identification methods for energy consumption proces-ses,a method for identifying and predicting energy consumption of machine tools was proposed by combining integrated models and deep learning.Taking CNC milling as an example,an energy con-sumption model was established based on different cutting periods,and signals were preprocessed by wavelet transform.The preprocessed signals were used to train and predict the energy consumption of the model combining RF and LSTM neural network(RF-LSTM model).Meanwhile,the RF was used to identify the cutting stages and realize the energy consumption classification prediction.The effec-tiveness and superiority of the proposed method were demonstrated through practical cases,and the RF-LSTM model was used to compare with the other four schemes,which verify that this recognition method may accurately predict different operating states and energy consumption of the machine tools.
