Advanced Model Predictive Control by T. Zheng

By T. Zheng

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Torque references and vehicle load) are entirely known could be perhaps an acceptable assumption. In our simulations, the future driving cycle is unknown whilst retaining constant for the whole horizon of N samples. However, if the future driving cycle could be entirely known, the performance of the proposed FMPC would be superior than those shown here. Fast Model Predictive Control and its Application toControl Energy of Hybrid Electric Fast Model Predictive and its Management Application to Energy Management of Hybrid ElectricVehicles Vehicles 23 21 Figure 4 shows a typical simulation results for the period of 20 secs in tracking requested setpoint HEV torques.

J et al, 2003]. The structure of the nonlinear model and the way it is used on-line affect the accuracy, the computational burden and the reliability of nonlinear MPC. Several different attempts to reduce computational complexity have been released during the last thirty years. The simplest way to reduce on-line computation is to transform the NMPC problem into LMPC. J et al,1997]. ,1998], when the linear model is used to predict future process behavior and the nonlinear model is used to compute the effect of the past input moves.

2 Fast Nonlinear Model Predictive Control using Second Order Volterra Models Based Multi-agent Approach Bennasr Hichem and M’Sahli Faouzi Institut Supérieur Des Etudes Technologiques de SFAX Ecole Nationale d’ingénieur de Monastir Tunisia 1. Introduction Model predictive control (MPC) refers to a class of computer control algorithms that utilize a process model to predict the future response of a plant. During the past twenty years, a great progress has been made in the industrial MPC field. Today, MPC has become the most widely implemented process control technology.

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