A modular design of input-to-state stable backstepping adaptive control is proposed in the presence of model
nonlinearity and parameters uncertainty for high maneuvers flight. An input-to-state stable backstepping controller is
designed based on modular strategy to guarantee state boundedness in the presence of bounded input signals. The
identification modular comprised of parameters adaptive law based on least squares algorithm and filters is designed. The
boundedness of parameters estimation errors and their derivatives is ensured, which is separated from input-to-state stable
controller modular. A modified particle swarm optimization based on immune clone principle is used to optimize controller
fixed parameters for achieving good transient performance. Simulation results show the effectiveness of the control algorithm.