design a pid controller for suspension system by back propagation neural network

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ID: 237401
2013
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Abstract
This paper presents a neural network for designing of a PID controller for suspension system. The suspension system, designed as a quarter model, is used to simplify the problem to one-dimensional spring-damper system. In this paper, back propagation neural network (BPN) has been used for determining the gain parameters of a PID controller for suspension system of automotive. The BPN method is found to be the most accurate and quick. The best results were obtained by the BPN by Levenberg-Marquardt algorithm training with 10 neurons in the one hidden layer. Training was continued until the mean squared error is less than . Desired error value was achieved in the BPN, and the BPN was tested with both data used and not used for training. By training of this network, it is possible to estimate the gain parameters of PID controller at any condition. The inputs of network are automotive velocity, overshoot percentage, settling time, and steady state error of suspension system response. Also outputs of the net are the gain parameters of PID controller. Resultant low relative error value of the ANN model indicates the usability of the BPN in this area.
Reference Key
heidari2013journaldesign Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;M. Heidari;H. Homaei
Journal Plant foods for human nutrition (Dordrecht, Netherlands)
Year 2013
DOI
10.1155/2013/421543
URL
http://dx.doi.org/10.1155/2013/421543
Keywords
engineering (general) civil engineering (general)

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