Simultaneous Unknown State and Input Estimation With Application to Virtual Air Charge and EGR Sensors for Automotive Engines

This paper addresses the problem of simultaneous estimation of unknown state and input for a class of nonlinear systems. Under certain minimum-phase conditions, the unknown states and inputs can be recovered asymptotically with high gain observers. The design of the observer uses the perfect observation scheme such that a restrictive input-output rank condition can be relaxed. It can be shown that the proposed estimation approach of the unknown input is in the form of the integration of estimation error signals. Thanks to the integrator the common issue of measurement noise corruption of the recovered unknown inputs can be significantly reduced. The proposed approach is applied to real-time estimate in-cylinder air charge and exhaust gas recirculation (EGR) flow rate of automotive internal combustion engines which are important factors for tailpipe emission reduction.