NN-UDE based fractional-mixed-order consensus tracking control of uncertain heterogeneous systems

This paper investigates fractional-order leader-following consensus control problems in the presence of uncertainties and disturbances, which provides solutions to heterogeneous-target-tracking especially in cross-domain operations where the targets may display fractional-order dynamics when moving on different surfaces. On the methodology side, Neural Network (NN) and Uncertainty and Disturbance Estimator (UDE) are adopted to approximate the fractional-order behavior and estimate unknown model structures of the agents respectively, so that the controller leads to robustness of the convergence. The NN-UDE combination results in complementary strengths to avoid their drawbacks: the requirement of model information by UDE and the high computational cost of NN. Theoretical analysis, as well as simulation experiments, are conducted to exhibit that the coupling of the two methods offers better computational efficiency and chattering elimination.