Exact Optimization-Free Safety Filters for Control Barrier Functions

摘要

For control-affine systems, standard and high-order control barrier function conditions are affine in the control input and are commonly enforced through quadratic-program-based safety filters. Although convex, these optimization problems may be undesirable in embedded, high-rate, or resource-limited implementations. This letter studies when the corresponding Euclidean projection can be computed exactly without solving a quadratic program. Given a nominal control input, we form the set of affine inequalities violated by that input and compute the minimum-norm correction that enforces those inequalities with equality. This correction need not equal the exact Euclidean projection onto the full feasible set. The main result gives structural conditions under which it coincides with the Euclidean projection onto the feasible set. These conditions are interpreted through interactions between affine-inequality normals and are expressed using a Gram matrix. Finally, an online certification procedure is given for determining whether the optimization-free update is exact.