An MPC design flow for automotive control and applications to idle speed regulation

Abstract

This paper describes the steps of a model predictive control (MPC) design procedure developed for a broad class of control problems in automotive engineering. The design flow starts by deriving a linearized discrete-time prediction model from an existing simulation model, augmenting it with integral action or output disturbance models to ensure offset-free steady-state properties, and tuning the resulting MPC controller in simulation. Explicit MPC tools are employed to synthesize the controller to quickly assess controller complexity, local stability of the closed-loop dynamics, and for rapid prototype testing. Then, the controller is fine-tuned by refining the linear prediction model through identification from experimental data, and by adjusting from observed experimental performance the values of weights and noise covariances for filter design. The idle speed control (ISC) problem is used in this paper to exemplify the design flow and our vehicle implementation results are reported.