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.
Highlights
Vehicle suspension serves as the basic function of isolating passengers and the chassis from the roughness of the road to provide a more comfortable ride
The present study shows that for the analyses of PID controller of suspension system, the back propagation neural network (BPN) is a suitable method
The BPN was successfully applied for determining the gain parameters of a PID controller for a suspension system
Summary
Vehicle suspension serves as the basic function of isolating passengers and the chassis from the roughness of the road to provide a more comfortable ride. Due to developments in the control technology, electronically controlled suspensions have gained more interest These suspensions have active components controlled by a microprocessor. The design of vehicle suspension systems is an active research field in which one of the objectives is to improve the passenger’s comfort through the vibration reduction of the internal engine and external road disturbances [1,2,3]. A methodology for the design of active/hybrid car suspension systems with the goal to maximize passenger comfort (minimization of passenger acceleration) was presented by Spentzas and Kanarachos [9]. For this reason, a neural network (NN) controller was Journal of Engineering
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