Discrete-Time LPV Observer With Nonlinear Bounded Varying Parameter and Its Application to the Vehicle State Observer

In this paper, we designed a unique structure and developed a novel linear parameter varying (LPV) formulation design for a vehicle-model-based state observer. The design problem for a vehicle state observer was totally reformulated into an LPV system that depended on online accessible time-varying parameters. The proposed LPV formulation, which is presented for the first time in this paper, did not require high computational costs compared to a conventional approach using an extended Kalman filter (EKF) including a nonlinear term. With regard to vehicle state estimation using a global positioning system, we considered the measured course angle as a varying parameter. Observer gain scheduling was determined using an H2filter based on the linear matrix inequality approach. The state observer gain using a discrete-time H2filter could minimize the error from a disturbance to a state. The stability of the proposed algorithm is proven, and the performance of the proposed method was demonstrated with an experimental test through a performance comparison with the EKF.