Mingxin Kang

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This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the(More)
This paper presents a model predictive online optimization scheme for the engine torque control problem. The control-oriented model is based on the intake air charging dynamics and torque generation model which are derived from the mean value model. In order to reduce the tracking error induce by the insufficient accurate predictive model, an embedded(More)
This paper presents a multi-variable optimal controller design based on model predictive control (MPC) scheme for automotive gasoline engines. The optimal control aims to achieve fast torque tracking with the lower pumping loss, by tuning the throttle valve angle and the intake valve timing. To this end, the nonlinear engine model was built based on the(More)
This paper presents a model-based receding horizon optimal control algorithm for the engine speed tracking control. A mean-value model including the air intake dynamics and the rotational dynamics is exploited in the tracking controller design, which is calibrated based on the physical rules combined with curve fitting techniques. Based on this mean-value(More)
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(More)
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