Model Predictive Control of Nonlinear Systems

Model Predictive Control (MPC) has the potential, not easily provided by other methods, to stabilize linear and nonlinear systems with state and control constraints. In the process control literature a simple, finite horizon, objective function is employed which does not, per se, guarantee stability; this is obtained by a suitable choice of some parameters in the objective function. The 'system theory' literature, on the other hand, focusses on the stability issue, and shows that by adding an appropriate stability constraint to the finite horizon objective function, stability can be insured. This paper explores the possibility of combining the virtues of both approaches.