For variable speed pump-controlled hydraulic cylinder system,the nonlinear change of hydraulic system parameters is brought in by large-scale change of speed or load.It causes the control system,which is designed by the linear model,to have the problems such as difficult correction of control parameters,unstable precision or even control instability.In this paper,a multi-model adaptive PID(MMA-PID)control method is proposed by analyzing the state space of a typical variable speed pump-controlled hydraulic cylinder system.According to the nonlinear change of the bulk elastic modulus of oil caused by the change of the system pressure,the system behavior is described by using multiple linear sub-models.A reasonable controller is designed for each sub-model.During the control process,the output weight coefficient of each sub-model is estimated separately through the Kalman filter,and the weighted fusion of all the sub-models control output is used as the final control input of the system.The simulation and experimental results demonstrate that when the working conditions are vary widely,the MMA-PID can adapt to the nonlinear change of system parameters better than the traditional PID,and it owns better control effect and dynamic performance.

The pressure pulsation of axial piston pump is not only an important cause of rotation speed fluctuation,vibration noise and output stability of the hydraulic system,but also the main information source for obtaining fault information.Hydraulic system is characterized by strong noise interference,which leads to low signal-to-noise ratio(SNR)of detection signals.Therefore,it is necessary to dig deep into the system operating state information carried by pressure signals.Firstly,based on flow loss mechanism of the plunger pump,the mapping relationship between flow pulsation and pressure pulsation is analyzed.After that,the pressure signal is filtered and reconstructed based on standard Gabor transform.Finally,according to the time-domain waveform morphology of pressure signal,four characteristic indicators are proposed to analyze the characteristics of pressure fluctuations under different working conditions.The experimental results show that the standard Gabor transform can accurately extract high-order harmonics and phase frequencies of the signal.The reconstructed time-domain waveform of pressure pulsation of the axial piston pump contains a wealth of operating status information,and the characteristics of pulsation changes under various working conditions can provide a new theoretical basis and a method support for fault diagnosis and health assessment of hydraulic pumps,motors and key components.

Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realize the condition monitoring of hydraulic system.The early fault characteristics of hydraulic gear pump hidden in the motor current signal are weak and difficult to extract by traditional time-frequency analysis.Based on the correlation coefficient and artificial bee colony algorithm (ABC),the parameter optimization of variational mode decomposition (VMD)is realized in this paper.At the same time,the principle of maximum signal correlation coefficient and kurtosis value is adopted to determine the effective intrinsic mode function (IMF).Moreover,the permutation entropy(PE)and root mean square(RMS)of the effective IMF components are input into the deep belief network (DBN-DNN)as high-dimensional feature vectors.The operation state of gear pump is monitored.The results show that the weak characteristics of current signal of gear pump fault are accurately and stably extracted by this method.The running state of gear pump is monitored and the accuracy of gear fault diagnosis is improved.