Adaptive Particle Swarm Optimized Trajectory Tracking of Unmanned Helicopter Under Unmatched Disturbances

Sliding mode control (SMC) is a good candidate in alleviating disturbances, but is problematic because of the chattering phenomenon. Super twisting control (STC), is an extension of SMC, which is continuous in nature. A control scheme that combines both STC and linear quadratic regulator (LQR) has been devised to make a small-scale 6-DoF helicopter model trace a pre-defined trajectory. The STC has been designed to reject both matched and unmatched forms of disturbances. Matched disturbances are those that creep in via the input channel to the system. Unmatched disturbances are the external factors that affect the system dynamics directly, such as wind gusts. The weighing matrices for the LQR control and the projection matrix of STC (for matched disturbance) have been optimized using adaptive particle swarm optimization technique. The simulation results presented show the efficacy of the controller in achieving the post stated control objectives.