Explicit min-max model predictive control of constrained nonlinear systems with model uncertainty
Abstract
This paper presents an approximate multi-parametric nonlinear programming approach to explicit solution of constrained nonlinear model predictive control (MPC) problems in the presence of model uncertainty. The case of time-invariant parameter uncertainty is considered. The explicit MPC controller is based on an orthogonal search tree structure of the state space partition and is designed by solving a min-max optimization problem. It is robust in the sense that all constraints are satisfied for all possible values of the uncertain parameters. The approach is applied to design an explicit min-max MPC controller for a continuous stirred tank reactor, where the heat transfer coefficient is an uncertain parameter.
