Adaptive Identification and Application of Flow Mapping and Inverse Flow Mapping for Electrohydraulic Valves
DOI:
https://doi.org/10.13052/ijfp1439-9776.2315Keywords:
identification, flow mapping, inverse flow mapping, electrohydraulic valve, LS, BP, RBF, GRNN, LSSVM, RLSAbstract
Good estimation of flow mapping (FM) and inverse flow mapping (IFM) for electrohydraulic valves are important in automation of fluid power system. The purpose of this paper is to propose adaptive identification methods based on LSM, BPNN, RBFNN, GRNN, LSSVM and RLSM to estimate the uncertain structure and parameters in flow mapping and inverse flow mapping for electrohydraulic valves. In order to reduce the complexity and improve the identification performance, model structures derived from new algorithm are introduced. The above identification methods are applied to map the flow characteristic of an electrohydraulic valve. With the help of novel simulation architecture via OPC UA, the accuracy and efficiency of these algorithms could be verified. Some issues like invertibility of flow mapping are discussed. At last, places and suggestions to apply these methods are made.
Downloads
References
A Vahidi, A Stefanopoulou and H Peng. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Mechanical Engineering Dept., University of Michigan, Ann Arbor.
C Kamali, A A Pashikar and J R Raol. Evaluation of recursive least squares algorithm for parameter estimation in aircraft real time applications. Aerospace Science and Technology 15, pp. 165–174, 2011.
S Dong, L Yu, W A Zhang and B Chen. Robust extended recursive least squares identification algorithm for Hammerstein systems with dynamic disturbances. Digital Signal Processing 101, 102716, 2020.
M Kazemi and M M Arefi. A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems. ISA Transaction, 2016.
S Linnainmaa. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master’s Thesis, 1970.
D J Jwo, K P Chin. Applying Back-propagation Neural Networks to GDOP. Journal of Navigation, 55, pp. 97–108, 2002.
C S Chen and S L Su. Resilient Back-propagation Neural Network for Approximation 2-D GDOP. Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol II, pp. 1–5, 2010.
C S Chen, J M Lin and C T Lee. Neural Network for WGDOP Approximation and Mobile Location. Mathematical Problems in Engineering, Vol. 2013, pp. 1–11, 2013.
O Nelles and R Isermann. A Comparison Between RBF Networks and Classical Methods for Identification of Nonlinear Dynamic Systems. IFAC Adaptive Szstems in Control and Signal Processing, pp. 233–238, 1995.
M Y Mashor, Some properties of RBF network with applications to system identification. IJCIM Volume 7, pp. 1–37 1999.
H Yijun and N Wu. Application of RBF Network in System Identification for Flight Control Systems. 2010 International Forum on Information Technology and Applications (IFITA), pp. 67–69, 2010.
C Pislaru and A Shebani. Identification of Nonlinear Systems Using Radial Basis Function Neural Network. International Scholarly and Scientific Research & Innovation, Vol. 8, pp. 1528–1533, 2014.
J B d A Rego, A d M Martins and E d B Costa. Deterministic System Identification Using RBF Networks. Mathematical Problems in Engineering, Vol. 2014, pp. 1–10, 2014.
S Khan, I Naseem, R Togneri and M Bennamoun. A Novel Adaptive Kernel for the RBF Neural Networks. Circuits Systems and Signal Processing. Band 36. pp. 1639–1653, 2017.
L Marquez and T Hill. Function approximation using backpropagation and general regression neural networks. Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences. IEEE. pp. 607–615, 1993.
S Yang, T O Ting et al. Investigation of Neural Networks for Function Approximation. Procedia Computer Science 17. pp. 586–594, 2009.
T Khawaja, G Vachtsevanos. A novel Bayesian Least Squares Support Vector Machine based Anomaly Detector for Fault Diagnosis. Annual Conference of the Prognostics and Health Management Society. pp. 1–8, 2009.
T Van Gestel, J A K Suykens. Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis. Neural Computation 14, pp. 1115–1147, 2002.
K Liu, B Y Sun. Least Squares Support Vector Machine Regression with Equality Constraints. Physics Procedia. pp. 2227–2230, 2012.
B R Andersson. On the Valvistor, a proportionally controlled seat valve. Linköping Studies in Science and Technology. Dissertations. No. 108, 1984.
E Prasetiawan, R Zhang, und A Alleyne. Fundamental performance limitations for a class of electronic two-stage proportional flow valves. Proceedings of American Control Conference, pp. 3955–3960, 2001.
A Sitte, O Koch, J Liu, R Tautenhahn, J Weber. Multidimensional flow mapping for proportional valves. 12th International Fluid Power Conference, Dresden, Group F, pp. 231–240, 2020.
R Isermann, M Münchhof. Identification of Dynamic Systems – An Introduction with Applications. ISBN 978-3-540-78878-2, pp. 324–332, 2011.
Akaike, Hirotugu. Information Theory and an Extension of the Maximum Likelihood Principle. In Selected Papers of Hirotugu Akaike, edited by Emanuel Parzen, Kunio Tanabe, and Genshiro Kitagawa, 199–213. New York: Springer, 1998.
Akaike, Hirotugu. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control 19, no. 6 (December 1974): 716–23.
Burnham, Kenneth P., and David R. Anderson. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed, New York: Springer, 2002.
Schwarz, Gideon. Estimating the Dimension of a Model. The Annals of Statistics 6, no. 2 (March 1978): 461–64.
J Liu, Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, Springer Berlin Heidelberg, p. 24, 2013.
