We present a new forward chaining planner, TALplanner, based on ideas developed by Bacchus and Kabanza, where domain-dependent search control knowledge represented as temporal formulas is used to effectively control forward chaining. Instead of using a linear modal tense logic as with Bacchus and Kabanza, we use TAL, a narrative-based linear temporal logic used for reasoning about action and change in incompletely specified dynamic environments. Two versions of TALplanner are considered, TALplan/modal which is based on the use of emulated modal formulas and a progression algorithm, and TALplan/non-modal which uses neither modal formulas nor a progression algorithm. For both versions of TALplanner and for all tested domains, TALplanner is shown to be considerably faster and requires less memory. The TAL versions also permit the representation of durative actions with internal state.