LAUSR

Currently, mechatronic systems are commonly used in automotive control systems to improve ride comfort and road safety characteristics. In these control systems, the sensors used to determine the input signals of controllers are very important to help the calculation process and give the correct control output signals for actuators. However, not all input signals can be easily measured directly by sensors or their cost is too high. This paper proposes one using the Kalman–Bucy estimator to estimate the state variables of a two-axle car to study the automotive suspension system. The disturbance input is the road profile at the four tires, while the car speed is considered from 40 km/h to 120 km/h. This research only uses four signals, namely, the vertical displacement of the four unsprung masses, through the estimator it can determine 14 variables of the vector state of the general vertical model of the car. The advantage of this research is that it is possible to reduce the number of the sensors but still ensure that the necessary input signals are obtained for studying the full car model. The signal estimation through some sensors available on the car is necessary for practice. Survey results show that the signal quantification through the Kalman–Bucy estimator unit has an accuracy of over 96% compared to the original car’s signals.