Adaptive Control Strategy of a Kalman Filter Active Vehicle Suspension

In this paper, the Kalman filter algorithm is used to design a practical adaptive control strategy. The adaptation is intended to adjust the system operation according to the changes of road input. A moderate adaptive time of at least 3 seconds is used. Limit stops are added to prevent the increase in the wheel travel behind the specified limit. The active suspension feedback system is designed based on measuring only the suspension displacement. A gain scheduling adaptive scheme which consists of four sets of state feedback gains is designed. The estimation process of dynamic tyre deflection and other necessary state variables through the Kalman filter is illustrated. Among other things, this estimate is used to derive the gain scheduling adaptive scheme. The strategy is applied to a quarter car active suspension system. Results are generated at a constant speed on random road profiles.

The present results indicate that, the reduction obtained in root mean square values of the body acceleration by the adaptive system is always associated with an increase in the dynamic tyre load root mean square values. For rough road surfaces, where the wheel control is very important, the fixed parameters active suspension system shows the same performance level as that of the adaptive active suspension system. On smooth and medium road surfaces, where the control of the tyre is not critical, the adaptive system shows the capability of improving the ride comfort in comparison with the fixed parameter active suspension system. It is shown that the effectiveness and importance of adaptation control is dependent on the control strategy and on the operating conditions.