Nariman Sepehri

Design and experimental evaluation of a robust force controller for an electro-hydraulic actuator via quantitative feedback theory

Abstract This paper presents the design and experimental evaluation of an explicit force controller for a hydraulic actuator in the presence of significant system uncertainties and nonlinearities. The nonlinear version of quantitative feedback theory (QFT) is employed to design a robust time-invariant controller. Two approaches are developed to identify linear time-invariant equivalent model that can precisely represent the nonlinear plant, operating over a wide range. The first approach is based on experimental input–output measurements, obtained directly from the actual system. The second approach is model-based, and utilizes the general nonlinear mathematical model of a hydraulic actuator interacting with an uncertain environment. Given the equivalent models, a controller is then designed to satisfy a priori specified tracking and stability specifications. The controller enjoys the simplicity of fixed-gain controllers while exhibiting robustness. Experimental tests are performed on a hydraulic actuator equipped with a low-cost proportional valve. The results show that the compensated system is not sensitive to the variation of parameters such as environmental stiffness or supply pressure and can equally work well for various set-point forces.

A measure of machine stability for moving base manipulators

This paper presents a scheme to monitor the potential of tipping over for moving base manipulators. The method of energy stability developed by Messuri and Klein (1985) is extended here to quantitatively include the effect of all factors relevant to the stability of moving base manipulators. These factors include vehicle top-heaviness, rugged terrain conditions, inertial and external reactions arising from the manipulation of the implement. The application of this study is directed at teleoperated heavy-duty hydraulic machines that are used in forestry and construction industries. Simulation studies show that the inertial loadings are important in determining the stability of such machines and thus should not be overlooked.

Coordinated and Force-Feedback Control of Hydraulic Excavators

The human interface of a Caterpillar 325FB feller-buncher was modified to allow the operator to use (i) a 5-DOF joystick, and (ii) a 6-DOF magnetically-levitated joystick with stiffness feedback. While the operator commanded the velocity of the endpoint, an onboard computer system managed total system power, solved the inverse kinematics, servoed the joint actuators, and controlled the magnetically-levitated joystick.

A Wavelet-Based Approach to Internal Seal Damage Diagnosis in Hydraulic Actuators

This paper describes the application of wavelet transform (WT) to detect internal leakage in hydraulic actuators, caused by seal damage. The method analyzes the pressure signal at one side of the actuator in response to periodic step inputs to the control valve. It is shown that the detailed version of decomposed pressure signal, using discrete WT, establishes feature patterns that can effectively detect internal leakage and its severity. The proposed scheme requires a baseline (threshold) value, predetermined first by analyzing the pressure signal of a healthy actuator. Once the root mean square (rms) of the level-two detail coefficient values, obtained from the measured pressure signals in subsequent offline tests, fall below this baseline, a fault alarm is triggered. Furthermore, the degree of changes of the rms value from the one obtained under normal operating condition indicates the severity of fault. Experimental tests show promising results for detecting internal leakages as low as 0.124 L/min, representing approximately 2.6% reduction of flow rate available to move the actuator. This is done without a need to model the actuator or leakage. Other methods of leakage fault diagnosis require the model of the actuator or leakage fault. Furthermore, no other method reported the internal leakage detection of magnitude as low as the one reported in this paper.

Diagnosis of process valve actuator faults using a multilayer neural network

This paper investigates the ability of a multilayer neural network to diagnose actuator faults in a Fisher-Rosemount 667 process control valve. A software package that comes with the valve is used to obtain experimental figures of merit related to the position response of the valve given a step command. The particular values of the dead time, peak time, percent overshoot, steady state error, 63% and 86% rise times, and gain are shown to depend on the severity of three commonly occurring faults: incorrect supply pressure, actuator vent blockage, and diaphragm leakage. The relationships between these parameters form fault signatures for each operating condition that are subsequently learned by a multilayer feedforward neural network. The results show that the trained network has the capability to detect and identify various magnitudes of the faults of interest. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training. The approach presented in this paper allows the existing instrumentation to be utilised without modification. Thus, the proposed methodology is practical to implement.

Parametric fault diagnosis for electrohydraulic cylinder drive units

A novel model-based methodology for fault diagnosis (FD) of nonlinear hydraulic drive systems is presented in this paper. Due to its linear dependence upon parameters, a second-truncated Volterra nonlinear model is first used to characterize such systems. The versatile order-recursive estimation scheme is employed to determine the values of parameters in the Volterra model. The scheme also avoids separate determination of the model order; thus, the complexity of the search process is reduced. Next, it is shown that the estimated parameters, representing different states of the system, normal as well as faulty conditions, can be used to detect and isolate system faults in a geometric domain. Very promising results are exhibited via simulations as well as laboratory experiments. It is concluded that the developed parametric FD technique has potential to provide efficient condition monitoring and/or preventive maintenance in hydraulic actuator circuits.

Hydraulic Actuator Leakage Fault Detection using Extended Kalman Filter

Abstract This paper presents the application of extended Kalman filter (EKF) in order to identify leakage faults in hydraulically powered actuators. A hydraulic actuator can suffer from two types of leakages: internal or cross-port leakage at the piston seal and, external leakage at the shaft seal or the connecting pipes. An EKF-based estimator is constructed that includes complete nonlinear models of hydraulic functions as well as inevitable stick-slip friction in the actuator. It is shown that, firstly, under normal (no-fault) operating condition, the developed estimator closely predicts the states of the system, using only a few basic measurements. Secondly, in the presence of leakage faults, the level of residual errors between the estimated and the measured line pressures, increase significantly indicating the occurrence of faults. Thirdly, different leakage types can be identified by mapping the residual errors changes. Experiments are performed on a laboratory-based hydraulic actuator circuit. The results demonstrate the efficacy of the proposed EKF-based fault detection scheme to promptly and reliably respond to actuators external and internal leakage faults.

Hydraulic actuator circuit fault detection using extended Kalman filter

This paper presents the application of extended Kalman filter (EKF) to closely estimate the states of hydraulic systems. The EKF, working concurrently with the actual system, accepts the input voltage signal to the servovalve and the measurements of the cylinder chamber pressures, and then rebuilds the system states. The spool displacement of the servovalve, the chamber pressures of the actuator and the velocity of the ram are accurately estimated. By comparing the estimated states and the actual measurements, residual signals are generated and then analyzed to report the occurrence of faults. Experiments show that the EKF estimator has the ability to promptly and reliably respond to the changes in the pump pressure as little as 10% of the normal operating pressure.

Modeling and identification of electrohydraulic servos

In general, design of adaptive controllers requires a proper model of the actual plant. This is normally done by considering a discrete-time linear model and estimating its unknown parameters, periodically. In this note, we discuss some practical issues concerning the identification of electrohydraulic actuators, using discrete-time linear models.

A computer-aided process planning model based on genetic algorithms

Abstract A process planning model is developed in conjunction with a genetic algorithm. The model first receives the flow network description of possible process plans. The goal is to find the best plan according to some prescribed objective criteria. A novel stage binary matrix format is then applied to describe each stage in a flow network. This formulation allows proper representation of possible routes as binary strings to be used by a genetic algorithm. Each string is directly mapped onto a zero/one stage matrix. A “zero” bit in these strings represents no connection between two nodes in the flow network, whereas a “one” bit denotes a connection between the corresponding nodes. The binary stage matrix representation also facilitates the employment of a modification procedure to ensure that a single distinct path (indication of a process plan) exists in each individual network. The model, developed in the paper, is first illustrated using a simple network. The performance of the computer program based on this model is then evaluated for different sizes of networks.