A New Approach to Model Predictive Control Based on Two Degrees of Freedom Control and B-Splines Input Shaping

The purpose of this article is to reduce some technical difficulties related the complexity stability and feasibility analysis Model Predictive Control (MPC) as well relative optimization procedure. new approach based on a two degrees freedom control scheme where output $r(k)$ feedforward input estimator used forcing closed-loop system $\Sigma _f$ . This latter given by feedback connection Linear Time Invariant (LTI) plant with dynamic controller. task controller guarantee , fulfillment hard constraints for any satisfying an “ priori ” determined admissibility condition. computed through online minimization quadratic cost functional applied according usual MPC strategy. To simplify constrained problem, assumed be B-spline function. greatly decreases number decision variables procedure because B-splines are universal approximators which admit parsimonious parametric representation. Moreover such parameterization allows us reformulate box Constrained Least Squares (CLS) problem. It shown that recursive adopted strategy guaranteed in advance, regardless chosen prediction horizon.