A Receding Horizon Sliding Controller for Automotive Engine Coldstart: Design and Hardware‐in‐the‐Loop Testing With an Echo State Network High‐Fidelity Model

Abstract

AbstractThe aim of the current study is to probe the potential of receding horizon sliding control (RHSC) technique for reducing the coldstart hydrocarbon (HC) emissions of automotive spark‐ignited (SI) engines. The RHSC approach incorporates the potentials of sliding control (SC) and nonlinear model predictive control (NMPC) to employ the future information of the considered engine to keep the system's trajectories close to a stable manifold. To calculate the control commands, the authors adopt an efficient optimization technique, known as the multivariate quadratic fit sectioning algorithm (MQFSA), and also, define three different objective functions, based on l1, l2, and l∞ norms. To demonstrate the efficacy of RHSC controller, its performance is compared with two other well‐known controllers extracted from the literature, namely NMPC and Pontryagin's minimum principle (PMP)‐based controllers. Through numerical simulations for three distinctive operating conditions, it is demonstrated that the RHSC controller is very effective for reducing the total tailpipe HC emissions over the coldstart period of the considered engine system. Moreover, by conducting a hardware‐in‐the‐loop (HIL) test using an echo state network high‐fidelity model, it is indicated that the computational speed of calculating control commands is fast enough to enable RHSC to be used for real‐time implementations in practice.