Reducing computational load for LQ optimal predictive controllers

Predictive controllers allow for systematic inclusion of constraints. However, the associated quadratic programming problem is computationally burdensome especially when large numbers of degrees of freedom are required to ensure feasibility, that is simultaneous satisfaction of terminal conditions and system constraints. The paper develops algorithms which can utilise very few degrees of freedom and still meet constraints while also giving optimal performance when constraints are inactive.