Tracking of non-square nonlinear systems via model predictive control

This paper presents a new Model Predictive Control algorithm for discrete-time nonlinear systems. The method guarantees the stability of the equilibrium and asymptotic zero error regulation for asymptotically constant reference signals. Its main characteristic is the application of integral action directly on the error variable. In the case of nonsquare systems with more inputs than controlled variables, and differently from other popular schemes, this allows one to use the minimum number of integrators. The proposed approach is applied to the Tennessee Eastman control problem with satisfactory results.