A number of current planners make use of automatic domain analysis techniques to extract information such as state invariants or necessary goal orderings from a planning domain. There are also planners that allow the user to explicitly specify additional information intended to improve performance. One such planner is TALplanner, which allows the use of domain-dependent temporal control formulas for pruning a forward-chaining search tree. This leads to the question of how these two approaches can be combined. In this paper we show how to make use of automatically generated state invariants to improve the performance of testing control formulas. We also develop a new technique for analyzing control rules relative to control formulas and show how this often allows the planner to automatically strengthen the preconditions of the operators, thereby reducing time complexity and improving the performance of TALplanner by a factor of up to 400 for the largest problems from the AIPS-2000 competition.