On nonlinear distributed parameter model predictive control strategy: on-line calculation time reduction and application to an experimental drying process

It is now recognized that model predictive control (MPC) is an interesting alternative for real-time control of industrial processes. In the meantime, some problems do still remain in progress: for theoretical aspects, the a priori guarantee of the stability and for the practical aspects, the guarantee of sufficient time to solve to optimization problem at each sampled time positions. In this paper, we propose a global method that aims to reduce the on-line calculation time due to the PDE model based optimization task resolution. It is addressed for a particular class of systems not very often studied in this context: systems described by partial differential equations (PDEs) which are, in the present case, nonlinear and parabolic. In order to decrease the computational burden, the nonlinear PDE system is solved off-line. Then, a linearized PDE model around the previous off-line behavior is used to find the optimal variations for the on-line predictive control. The real-time control application given is concerned with a infrared drying process of painting film. [Copyright &y& Elsevier]