Papers by Keyword: Hydraulic Servo System

Abstract: Because hydraulic actuator has higher power and force density, it is normally used in heavy load manipulator robots and industrial equipment which require high torque. Also, the hydraulic actuator is applied to underwater robots that need high performance maneuver in underwater operations. The force control has benefits to those kind of robots to ensure compliance with user or environment. However, the hydraulic actuator is difficult to control forces due to the non-linearity characteristic of the hydraulic servo system. In this paper, we propose a force control method with compensation of force derivative and natural velocity feedback. We also describe a method of applying it to the real system. In order to evaluate the effect of the proposed control method, the simulations and experiments were performed.

Abstract: A hydraulic servo system is a typical feedback control system. Health assessment of a hydraulic servo system is usually difficult to realize when traditional methods based on sensor signals are utilized. An approach for health assessment of hydraulic servo systems based on multi-fractal analysis and Gaussian mixture model (GMM) is proposed in this study. A GRNN neural network is employed to establish a fault observer for the hydraulic servo system. The observer is utilized to simulate the system output under normal state. The residue is then generated by subtracting the estimated output from the actual output. The residue’s feature is extracted by fractal analysis. After the feature extraction, the overlap between the current feature vectors and the normal feature vectors is obtained by applying GMM. The confidence value (CV) can be obtained in advance; this value is employed to characterize the health degree of the current state and consequently implement the health assessment of the hydraulic servo system. Lastly, two common types of fault, namely, burst and gradual, are applied to validate the effectiveness of the proposed method.

Abstract: Hydraulic servo system is highly nonlinear. Building an accurate model of the system and predicting its remaining life are difficult. Thus, this study focuses on the prediction of the Hydraulic servo System based on Support vector regression (SVR). Elman neural network is utilized to build an observer to estimate the normal state output. The residual that contains a large amount of fault information is obtained, by calculating the difference between the estimated and actual values. Then we defined degradation index (DI) value which reflect the health of the system to normalize the residual. Lastly, a prediction model based on SVR established. The algorithm is verified by experiment.

Abstract: A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

Abstract: The performance assessment of hydraulic servo systems has attracted an increasing amount of attention in recent years. However, only a few studies have focused on practical approaches in this field. A performance assessment method based on radial basis function (RBF) neural network and Mahalanobis distance (MD) is proposed in this study; the method is quantized by the performance confidence value (CV). An observer model based on RBF neural network is designed to calculate the residual error between the actual and estimated outputs. The root mean square (RMS), peak value, and average absolute value are then extracted as the features of residual error, which serve as the coordinates of the feature points. Lastly, the MD between the most recent feature point and the constructed Mahalanobis space is calculated. The condition of the system is assessed by normalizing MD into a CV. The proposed method is proven to be effective by a simulation model in which leakage faults are injected. Experimental results show that the proposed method can assess the performance of hydraulic servo systems effectively.

Abstract: The sliding mode control approach based on double power exponential reaching law is proposed for the hydraulic servo system. With the example of the hydraulic servo system in the lab, the mathematic model is established and the new controller is presented and simulated. Simulation results show that: the proposed approach has high track precision, fast response, small chattering and ensures dynamic quality of the system.

Abstract: Based on the analysis of the development, defects and motion of present hydraulic system vibrating table, it is divided into several single disciplinary system modes which are organic related in this paper. Establishing unified digital model in integrated simulation environment through analyzing the function of these models, which will guide the design of digital vibration table system.

Abstract: Jam fault occurs most times in the hydraulic servo system, and is the one of the factors that mainly influences the reliability of the system. For parallel-operated turbine, the jam faults of regulating system are elusive and fatal. In order to monitor operation condition and improve the reliability of the system online, the model of jam fault had been built, the sizes of the dead zones which describe jam fault had been viewed as unmeasured parameters, which avoided the model uncertainty brought by the linearization of nonlinear system, and strong tracking filter had been used to estimate unmeasured parameter. Simulation and experimental results verified that, this technique could accurately, synchronously estimate respective degrees of jam faults of slide valve and pilot valve online whether in daily operation or moving test.

Abstract: To reduce the negative impact of the unexpected motion in the heave direction when hoisting on the sea, a heave compensation system is indispensable for a floating crane. In this paper, a genetic algorithm-based PID controller is presented to improve the dynamic performance of the heave compensation system. A simulation model of electro-mechanical and hydraulic integrated system of the heave compensation system is built. The simulation results show that the genetic algorithm-based PID control system improves the dynamic characteristics, strengthens the stability and rapidity of the system and even ensures the control effect of the heave compensation system.

Abstract: This paper states the control principle of the three-channel hydraulic loading test bench for automobile steering gear test. The test bench model considering inertial force is constructed and simulated. The Integral-Separated PID control algorithm is adopted to optimize control effect and the respond features of the input signals are analyzed. The simulation results show that the system works steadily, the accuracy of synchronization control is better and the tracking control achieves well.