As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles' local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.