On efficient predictive control of linear systems subject to quadratic constraints using condensed, structure-exploiting interior point methods

We describe a primal-dual interior point method tailored for predictive control of linear systems subject to convex quadratic costs and constraints. In particular, we consider only the inputs as decision variables to reduce the memory demand.Moreover, we propose to exploit the problem structure, which decreases the computational burden for long horizons. For the implementation we consider tailored solution methods of the arising Riccati recursion and the line-search. Furthermore, we discuss the memory and computational demand and problem specific methods to further save computations. Finally, we outline with an example the applicability.