A Decomposition Algorithm for a Class of Nonlinear Dynamic Systems with Cross-Sensitive Output Measurement

Abstract

Electrochemical sensors have been widely applied in various industries for monitoring and feedback control of air quality. However, many of the existing electrochemical sensors are cross-sensitive to interfering gases while measuring the target gases. In this study, we proposed a direct algebraic approach-based decomposition algorithm suitable for a class of nonlinear dynamic systems with cross-sensitive output measurements for state and output estimations. Nonlinear system state and output estimations, based on cross-sensitive output measurement, are rather challenging due to the surjective mapping from system states to direct output reading. The proposed algorithm utilizes the cross-sensitive output measurement and its time derivative information to systematically solve for the actual system states or outputs. The proposed algorithm was applied to the urea-based SCR system where the outlet NOx sensor suffers an ammonia cross-sensitivity issue while measuring NOx emissions. Simulation results over transient US06 cycle verified the effectiveness of the proposed decomposition algorithm in decoupling NOx concentrations and NH3 concentrations from the cross-sensitive NOx sensor readings, as well as in estimating the ammonia coverage ratio. The proposed decomposition algorithm can potentially be applied to a broad class of nonlinear dynamic systems with cross-sensitive electrochemical sensors to improve the system performance.