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

The 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.