Adaptive control concepts using radial basis functions and a Kalman filter for embedded control applications

Abstract

Look-up tables or semi-physical models are used in engine control application to enhance the transient control performance. However, the calibration of these look-up tables and/or semi-physical models is highly data dependent, requires significant calibration effort, and does not account for effects such as hardware aging, drifts, fuel variations, etc. To address these challenges, this paper presents a generic adaptive control concept for real-time engine control applications. The boundary conditions for the operation on a state-of-the-art engine control unit (ECU) are considered and Radial Basis Function (RBF)-based neural networks are identified for the approximation of non-linear models. To ensure optimal convergence and un-susceptibility to signal noise, a Kalman filter (KF) is exploited for training the RBF network and a modified approximation is derived in this work. The derived KF approximation shows ∼30% better training performance in comparison to the approximation without variable noise consideration. The developed control concept is validated on an engine test bench and a demonstrator vehicle using a Rapid Control Prototyping (RCP) system. The potential of the developed control concept to ensure optimal control response and robustness has been demonstrated.