Constrained receding horizon predictive control for nonlinear systems (Q1858346)

A constrained receding horizon predictive control (RHC) algorithm for nonlinear discrete-time systems is proposed. It relies on the on-line solution of a simple linear program (LP). In this RHC algorithm the linearized predicted dynamics about a suboptimal seed trajectory and the terminal inequality constraint are exploited. The inequality constraint ensures that the predicted terminal state belongs to a polytopic invariant set which is defined with respect to a locally stabilizing state feedback. In addition, the bounds on the linearization errors are derived. In order to ensure that the error bounds remain valid, a sequence of polytopic constraints are imposed on the linear prediction of the system state. The RHC is thus formulated in terms of linear inequalities as an LP problem. A simple example demonstrates the effectiveness of the resulting RHC strategy.