Model reference based intelligent control of an active suspension system for vehicles

An active suspension system is a kind of automotive suspension system which is used to enhance ride comfort, stability and safety while the load on the wheel and the suspension movement remain in safety limits. Several researches have been done in the past 15 decades in this field and many control methods were developed ranging from traditional controls to optimal and adaptive controllers. Robust and nonlinear control algorithms for suspension control are also notable now a days. Linear control schemes are robust and easy to implement but parameter uncertainty and nonlinear dynamics of actuator may reduce efficiency of such controllers. PID controllers are widely used control method because of its simplicity, but it lacks robustness in sudden changes in the parameters of a vehicle. Model predictive control is considered as one of the successful control scheme but due to multivariable interactions and time delay this control scheme is not effective in active suspension control. Nonlinear control schemes such as Artificial neural network controllers are more robust and efficient in Active suspension control. This paper come up with a model reference adaptive control scheme based on neural network for an Active suspension system. Modelling error is considered in this proposed control scheme to provide better adaptivity and stability for active suspension system under change in model parameters. A quarter car model with 2-DOF is selected for the analysis, which covers the vertical dynamics of vehicle. LQR is used as a benchmark controller and the performance of proposed controller is determined by carrying out computer simulations using MATLAB and SIMULINK.