Parametric data-driven control method of hybrid system via virtual reference feedback tuning

This paper presents a data-driven method to design parameterized controller for a class of hybrid system. The method relies on the virtual reference feedback tuning (VRFT) that is ongoing does not derive any detailed model knowledge of hybrid system. Unlike other model-based control methods, the VRFT method direct use of the measurement I/O data and select desired controllers by employing optimization tools from virtual reference approximation. Therefore, the optimal variables are directly as the control parameters of the hybrid system. To improve the efficiency of control parameter optimization, a particle swarm optimization (PSO) algorithm with a ε-dominance strategy is used to implement the developed method. In addition, the effectiveness of the method is demonstrated experimentally on a leader-following tracking task of multiple unmanned aerial vehicles (multi-UAVs) formation flight.