Abstract
Automated operations are widely used in harsh environments, in which position information is essential. Although sensors can be equipped to obtain high-accuracy position information, they are quite expensive and unsuitable for harsh environment applications. Therefore, a position soft-sensing model based on a back propagation (BP) neural network is proposed for direct-driven hydraulics (DDH) to protect against harsh environmental conditions. The proposed model obtains a position by integrating velocity computed from the BP neural network, which trains the nonlinear relationship between multi-input (speed of the electric motor and pressures in two chambers of the cylinder) and single-output (the cylinder’s velocity). First, the model of a standalone crane with DDH was established and verified by experiment. Second, the data from batch simulation with the verified model was used for training and testing the BP neural network in the soft-sensing model. Finally, position estimation with a typical cycle was performed using the created position soft-sensing model. Compared with the experimental data, the maximum soft-sensing position error was about 7 mm, and the error rate was within ±2.5%. Furthermore, position estimations were carried out with the proposed soft-sensing model under differing working conditions and the errors were within 4 mm, but the periodically cumulative error was observed. Hence, a reference point is proposed to minimize the accumulative error, for example, a point at the middle of the cylinder. Therefore, the work can be applied to acquire position information to facilitate automated operation of machines equipped with DDH.
Highlights
In order to meet the increasing energy efficiency requirements and adapt to dangerous and harsh environments, intelligent and autonomous have become the main development trend of heavy-duty or construction machinery [1]
The benefits of using virtual sensors include increased redundancy and reduced reliance on expensive position sensors typically used in hydraulic cylinders [2,3,4]
Gradl and Plockinger et al [19,20] carried out a study on position or speed control without displacement sensors and proposed a stepping hydraulic cylinder, which controlled the oil intake and discharge of two small driven cylinders by switching valves to realize the stepping of the main hydraulic cylinder, with a position accuracy of 0.17%
Summary
In order to meet the increasing energy efficiency requirements and adapt to dangerous and harsh environments, intelligent and autonomous have become the main development trend of heavy-duty or construction machinery [1]. Gradl and Plockinger et al [19,20] carried out a study on position or speed control without displacement sensors and proposed a stepping hydraulic cylinder, which controlled the oil intake and discharge of two small driven cylinders by switching valves to realize the stepping of the main hydraulic cylinder, with a position accuracy of 0.17%. This needs to redesign the loop, and more valve sets are required.
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