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Scalable and Invertible PMNN Model for MagnetoRheological Fluid Dampers

General Motors R&D, REB, 30500 Mound Road, Warren, MI 48071, USA, ming.cao{at}gm.com

K.W. Wang

Department of Mechanical and Nuclear Engineering, Pennsylvania State University, 157E Hammond Building, University Park, PA 16802, USA

Kwang Y. Lee

Department of Electrical and Computer Engineering, Baylor University, One Baylor Place 97356 Waco, TX 76798-7356, USA

To advance the state of the art of physical-principle-enhanced hybrid artificial neural network (ANN) modeling, network configurations with parallel modules (PMNN) reflecting the structural information of the physical principles have been developed (Cao, 2001). In this paper, the PMNN configuration is applied to develop a scalable and invertible dynamic magneto-rheological (MR) fluid damper model. To advance the state of the art and address issues of the current ANN-based MR damper models found in the open literature, two ANN-based MR damper models are developed in this study. The first one is a conventional first-principle-enhanced hybrid neural network model (defined as the baseline model in the current study) that improves upon the previous ANN-based MR damper models by introducing feedback loops to represent the dynamic behaviors of the MR damper. A PMNN-based MR damper model is then derived to further improve the control-input-output scalability and realize the invertible model concept. "Input-output scalability" refers to the model's capability to accurately estimate the system response with input profiles significantly different from the training data. "Invertible model" means that the resultant forward model can be directly transformed into an inverse model through a simple algebraic operation. The network training/testing results indicate that while both models provide satisfactory performance, the PMNN model outperforms the baseline model by showing superior control-input-output scalability. The candidacy of PMNN as a control-oriented actuator modeling tool is further strengthened by the fact that it is invertible, in other words, the inverse model with desired force as input and the control signal—voltage as output can be easily established by algebraically manipulating the forward model. This study indicates that PMNN, as a scalable and invertible dynamic modeling tool, is feasible for developing system-design-oriented models of vibration control purposes.

Key Words: Actuator • parallel modulated neural network (PMNN) • vibration control • dynamic modeling.

Journal of Vibration and Control, Vol. 14, No. 5, 731-751 (2008)
DOI: 10.1177/1077546307083988


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