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Pressure prediction on a variable-speed pump controlled hydraulic system using structured recurrent neural networks |
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| Affiliation: | 1. School of Transportation Science and Engineering, Beihang University, Beijing 100191, China;2. Beijing Key Laboratory for High-efficient Power Transmission and System Control of New Energy Resource Vehicle, Beihang University, Beijing 100191, China;3. College of IT and Engineering, Marshall University, Huntington, WV 25755, USA;1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044 China;2. College of Automotive Engineering, Chongqing University, Chongqing 400044 China |
| Abstract: | This paper presents a study to predict the pressures in the cylinder chambers of a variable-speed pump controlled hydraulic system using structured recurrent neural network topologies where the rotational speed of the pumps, the position and the average velocity of the hydraulic actuator are used as their inputs. The paper elaborates the properties of such networks in extended time periods through detailed simulation- and experimental studies where black-box modeling approaches generally fail to yield acceptable performance. As alternative estimation techniques, both linear- and extended Kalman filters are considered in this paper. The estimation properties of the devised network models are comparatively evaluated and their potential application areas are discussed in detail. |
| Keywords: | Pump controlled hydraulic system Pressure dynamics Long-term pressure prediction Structured recurrent neural networks Nonlinear system identification Kalman filtering |
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