LAUSR

The constrained linear quadratic regulation problem is solved by a continuous piecewise affine function on a set of state space polytopes. It is an obvious question whether this solution can be built up iteratively by increasing the horizon, i.e., by extending the classical backward dynamic programming solution for the unconstrained case to the constrained case. Unfortunately, however, the piecewise affine solution for horizon N is in general not contained in the piecewise affine law for horizon N + 1. We show that a backward dynamic programming does, in contrast, result in a useful structure for the set of the active sets that defines the solution. Essentially, every active set for the problem with horizon N + 1 results from extending an active set for horizon N , if the constraints are ordered stage by stage. Consequently, the set for horizon N + 1 can be found by only considering the constraints of the additional stage. Furthermore, it is easy to detect which polytopes and affine pieces are invariant to increasing the horizon, and therefore persist in the limit N → ∞. Several other aspects of the structure of the set of active sets become evident if the active sets are represented by bit tuples. There exists, for example, a subset of special active sets that generates a positive invariant and persistent (i.e., horizon invariant) set around the origin. It is very simple to identify these special active sets, and the positive invariant and persistent region can be found without solving optimal control or auxiliary optimization problems. The paper briefly discusses the use of these results in model predictive control. Some opportunities for uses in computational methods are also briefly summarized. 1 Problem statement and introduction We consider the constrained linear quadratic optimal control problem with finite and infinite horizons. The problem for finite horizon N reads V (x(0), [0, N ]) := min u(k), k=0,...,N−1 x(k), k=1,...,N 1 2 ‖x(N)‖P + 1