Model Predictive Control of Gasoline Engines with Nonlinear Feedback Linearized Model

Model predictive control (MPC) have received wide attention in many industrial field owing to its optimization capability for the practical control plant with constraints. However, the control performance of the closed-loop system is rarely considered in MPC design. In this paper, a relatively simple performance tuning approach for MPC-based engine speed control is investigated based on the inverse linear quadratic (ILQ) regulator design technique. Considering the nonlinear and time-varying properties of the engine system, the linear tracking dynamical system is deduced from the mean value model of gasoline engines by means of the feedback linearization method. Then the MPC optmization problem can be formulated with this linear tracking dynamics. In order to realize the convenient performance tuning, the MPC quadratic weightings are designed to be only related to a single tuning parameter according to the inverse optimality conditons of LQ problem. The experimental results validate the effectiveness of the proposed speed tracking controller and tuning method provides a trade-off between the response sensitivity and the magnitude of the control input.