A linear model predictive control algorithm for nonlinear large-scale distributed parameter systems

This work provides a framework for linear model predictive control (MPC) of nonlinear distributed parameter systems (DPS), allowing the direct utilization of existing large-scale simulators. The proposed scheme is adaptive and it is based on successive local linearizations of the nonlinear model of the system at hand around the current state and on the use of the resulting local linear models for MPC. At every timestep, not only the future control moves are updated but also the model of the system itself. A model reduction technique is integrated within this methodology to reduce the computational cost of this procedure. It follows the equation-free approach (see Kevrekidis et al., Commun Math Sci. 2003;1:715-762; Theodoropoulos et al., Proc Natl Acad Sci USA. 2000;97:9840-9843), according to which the equations of the model (and consequently of the simulator) need not be given explicitly to the controller. The latter forms a "wrapper" around an existing simulator using it in an input/output fashion. This algorithm is designed for dissipative DPS, dissipativity being a prerequisite for model reduction. The equation-free approach renders the proposed algorithm appropriate for multiscale systems and enables it to handle large-scale systems.