Optimum synthesis of sensory layout in active suspension based on linear observer

The dynamic response of a vehicle can be improved considerably by an active suspension, so it enjoys a growing popularity within urban cars. The vehicle vibrational models typically include many degrees of freedom, therefore the implementation of model-based feedback (feeding of state variables) control strategies in active suspension involves abundant measurements which lead to a big challenge. The observation technique is a conventional method employed to estimate the state variables and to reduce the number of output variables. As the number of measurements and corresponding sensors decrease, cost imposed to the system will decrease and the complexity of hardware implementation is also reduced. The purpose of this research is to investigate different output configurations and to determine the optimal sensory layout to achieve an ideal observer. Finally, a case study consisting of a four state quarter car model and two types of traditional sensors (acceleration and relative displacement) has been conducted. The presented algorithm takes a set of predefined sensors as input and analyses its various combinations. For each combination which forms a specific arrangement of outputs, the design metric is calculated through simulating assembled model of vehicle-controller-observer. This metric is a weighted sum of measure variables norm like passenger acceleration and estimation error.