This paper addresses the energy-inefficiency problem of four-degrees-of-freedom (4-DOF) hydraulic manipulators through redundancy resolution in robotic closed-loop controlled applications. Because conventional methods typically are local and have poor performance for resolving redundancy with respect to minimum hydraulic energy consumption, global energy-optimal redundancy resolution is proposed at the valve-controlled actuator and hydraulic power system interaction level. The energy consumption of the widely popular valve-controlled load-sensing (LS) and constant-pressure (CP) systems is effectively minimised through cost functions formulated in a discrete-time dynamic programming (DP) approach with minimum state representation. A prescribed end-effector path and important actuator constraints at the position, velocity and acceleration levels are also satisfied in the solution. Extensive field experiments performed on a forestry hydraulic manipulator demonstrate the performance of the proposed solution. Approximately 15-30% greater hydraulic energy consumption was observed with the conventional methods in the LS and CP systems. These results encourage energy-optimal redundancy resolution in future robotic applications of hydraulic manipulators.
Model-based condition monitoring methods are widely used in condition monitoring. They usually rely on ad hoc approaches to verify the system model and then best practices are reported to detect the given set of faults. This first part of a two-piece paper introduces a generic Global Sensitivity Analysis-based approach that can be applied systematically to verify the model parameter sensitivities used for the model-based fault detection. The case study is a generic servo valve-controlled hydraulic cylinder with unknown loading condition which is then systematically analyzed with Global Sensitivity Analysis. The method shows valuable insight into systematic model verification and resulting fault detection in terms of showing the dominant sensitivity of the nominal flow rate and nominal pressure difference, and the exact sensitivities of 0 -1 dm 3 /min external and internal leakages on cylinder chamber pressures and velocity. In the second paper, an Unscented Kalman Filter-based Fault Detection and Isolation scheme for leakage and valve faults of a generic servo valve-controlled hydraulic cylinder is devised and fault patterns are presented.
Hydraulic manipulators on mobile machines, whose hydraulic actuators are usually controlled by mobile hydraulic valves, are being considered for robotic closed-loop control. A feed-forward-based strategy combining position and velocity feedback has been found to be an effective method for the motion control of pressure-compensated mobile hydraulic valves that have a significant dead zone. The feed-forward can be manually identified. However, manually identifying the feed-forward models for each valve-actuator pair is often very time-consuming and error-prone. For this practical reason, we propose an automated feed-forward learning method based on velocity and position feedback. We present experimental results for a heavy-duty hydraulic manipulator on a forest forwarder to demonstrate the effectiveness of the proposed method. These results motivate the automated identification of velocity feed-forward models for motion control of heavy-duty hydraulic manipulators controlled by pressure-compensated mobile hydraulic valves that have a significant input dead zone.
A velocity feed-forward-based strategy is an effective means for controlling a heavy-duty hydraulic manipulator; in particular, a typical valve-controlled hydraulic manipulator, to compensate for valve dead-zone and other hydraulic valve nonlinearities. Based on our previous work on the adaptive learning of valve velocity feed-forwards, manually labelling and identifying the dead-zones and the other nonlinearities in the velocity feed-forward curves of pressure-compensated hydraulic valves can be avoided. Nevertheless, it may take two to three minutes or more per actuator to identify a pressure-compensated valve’s highly nonlinear velocity feed-forward in real-time with an adaptive approach, which should be reduced for realistic applications. In this paper, inspired by brain signal analysis technologies, we propose a new method based on deep convolutional neural networks comparing with the previous method to significantly reduce this online learning process with the strong nonlinearities of pressure-compensated hydraulic valves. We present simulation results to demonstrate the effectiveness of the deep learning-based learning method compared to the previous results with an adaptive control-based learning.
Leakages and valve faults are among the most common faults in hydraulic systems. This paper studies the real-time detection and isolation of certain leakage and valve faults based on the results obtained in part one. In the first part, the mathematical model of a hydraulic test bed was analysed with Global Sensitivity Analysis to facilitate a systematic and verified approach to model-based condition monitoring. In this paper, an Unscented Kalman Filter-based Fault Detection and Isolation scheme for leakage and valve faults of a generic servo valve-controlled hydraulic cylinder is devised. Compared to existing literature, the leakage and valve faults are decoupled from cylinder static and dynamic loading which makes the results generic and applicable to any servo valve-controlled hydraulic cylinder. Moreover, a more comprehensive set of fault patterns for the detection and isolation of leakages and valve faults with experimental and simulation results are presented. We show that detecting an external leakage of as small as 0.17 l/min is possible in some cases, but the accuracy of the method varies considerably. We also report why the isolation of valve faults from leakages is very difficult.
Detecting and isolating faults in complex engineering systems is important for properly planning maintenance, and it leads to decreases in down and repair times and to an increase in system performance. Mobile hydraulic valves, which are characterized by functions such as pressure-compensation and pilot-operation, are such complex systems that they could tremendously benefit from a real-time, accurate fault detection and isolation system. However, the complexity of these valves means that accurate full-state modeling is generally difficult and time-consuming. This paper proposes a reduced-order model for the fault detection of an open-loop-controlled mobile hydraulic valve. This reduced-order model is used along with statistically computed adaptive thresholds for the purpose of enhancing the reliability of fault detection. The considered faults include valve spool jamming caused by hydraulic fluid impurities, leakages caused by wear-induced increased clearances between the valve spool and sleeve, and sensor faults. The reduced-order model omits the modeling of pressure compensator dynamics by using a measurement of the pressure compensator, and pilot pressure dynamics by measuring the pilot pressures that drive the main spool. Experimental results from a commercial mobile hydraulic valve controlling a 2-DOF hydraulic crane show the practicality of the reduced-order modeling and adaptive threshold arrangements in this fault detection task. As a downside, a reduced set of faults can be isolated with the reduced-order model, but as a future consideration a bigger set of fault patterns with a full model are given and compared with the results obtained.
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