By T. Zheng
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Additional info for Advanced Model Predictive Control
Each corresponding to a different operating condition of the system and an interacting multiple model estimators is utilized to yield a reconstruction of the state of the non-linear system. For unconstrained control based on linear process models and a quadratic cost function, the control sequence can be analytically calculated. When linear constraints are taken into account, the solution can be found using quadratic programming techniques. With the introduction of a nonlinear model into MPC scheme, a nonlinear programming technique (NLP) has to be solved at each sampling time to compute the future manipulated variables in on-line optimization that is generally nonconvex which make their implementation difficult for real time control.
In classical model predictive control (MPC), the control action at each time step is obtained by solving an online optimization problem. If it is possible, MPC algorithms based on linear models should be used because of low computational complexity [Maciejowski J,2002]. Since properties of many technological processes are nonlinear, different nonlinear MPC techniques have been developed [Qin, S. 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.
Of the IEEE Int. Symp. On Vehicular Power and Propulsion, Paris, France. Husain, I. (2003). Electric and Hybrid Vehicles: Design Fundamentals, 1st edn, CRC Press. B. J. (2000). HEV control strategy for real-time optimization of fuel economy and emissions, In Proc. of the Future Car Congress, Washington DC, USA. Kaszynski, M. & Sawodny, O. (2008). Modeling and identiﬁcation of a built-in turbocharged diesel engine using standardized on-board measurement signals, Proc. of IEEE International Conference on Control Applications (CCA 2008), pp.