Fractional-Order Iterative Learning Control With Initial State Learning For A Class Of Multiagent Systems

To solve the consensus problem of fractional-order multiagent systems with nonzero initial states, both open- and closed-loop PD alpha-type fractional-order iterative learning control are presented. Considering the nonzero states, an initial state learning mechanism is designed. The finite time convergences of the proposed methods are discussed in detail and strictly proved by using Lebesgue-p norm theory and fractional-order calculus. The convergence conditions of the proposed algorithms are presented. Finally, some simulations are applied to verify the effectiveness of the proposed methods.