Constrained model predictive control with online input parametrization

The efficiency of model predictive control can be increased by the online choice of a low number of degrees of freedom in the parametrization of the input trajectory. The complexity of the optimization is reduced by searching a solution in a lower dimensional subset of the full input space. This is done by division of the set of constraints in a set of active, inactive and possibly active constraints. A low dimensional subset of the full input space is constructed that complies with the division of the constraints implying that the optimization complexity can be decreased considerably without sacrificing too much in terms of performance