A Monte-Carlo based model approximation technique for linear model predictive control of nonlinear systems

• We present a model approximation technique based on N-step ahead affine representations obtained via Monte-Carlo integrations.
• The approximations are derived from data generated from simulations of the original mathematical model.
• The approach is empirical and based on the calculation of conditional variances of the original model with respect to the system states and manipulated variables.
• The technique enables one to treat a nonlinear system with linear MPC (quadratic objective function).
• The approximation scheme shows performance similar to nonlinear model reduction techniques.

In this paper we present a model approximation technique based on N-step-ahead affine representations obtained via Monte-Carlo integrations. The approach enables simultaneous linearization and model order reduction of nonlinear systems in the original state space thus allowing the application of linear MPC algorithms to nonlinear systems. The methodology is detailed through its application to benchmark model examples.