Active Off-Road Seat Suspension System Using Intelligent Active Force Control

Abstract

Transmitted vibration from vehicles to driver body generates some problems in the long term. Passive and active seat suspensions are used in heavy duty vehicles to reduce unwanted vibration and prevent health problems due to oscillation. Seat suspension must minimize the driver's body displacement and acceleration to increase riding convenience. Active force control (AFC) method is a new technique which is used in active controllers and makes them more accurate. Therefore, this work represents the possibility of applying AFC strategy for an active seat suspension control to increase its robustness. An AFC-based scheme is designed and simulated in MATLAB software. In addition, artificial neural network (ANN) is integrated into the AFC loop to approximate estimated mass of the seat and human body for the proposed controller. The training of ANN with multi-layer feedforward structure is carried out using Levenberg-Marquardt learning algorithm. To evaluate the neuro-AFC control system robustness, the seat is subjected to various types of disturbances. The results of the present study illustrate that the neuro-AFC technique is computationally simple and efficient compared to the classic proportional-integral-derivative (PID) controller in suppressing undesired vibration of heavy duty vehicles' seat. The neuro-AFC scheme is found to demonstrate superior performance for various road profiles compared to pure PID controller, and it can be successfully utilized in heavy duty vehicles such as industrial and agricultural tractors.