Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control

Abstract In this paper, a distributed disturbance-compensation based model free adaptive iterative learning control (MFAILC) algorithm is proposed to achieve the consensus tracking of nonlinear multi-agent systems (MAS) with unknown disturbance. Here, both fixed and iteration varying topologies are considered. A general dynamic linearization model with disturbance input is first proposed to each agent along the iteration axis for nonlinear MAS. Due to the existence of unknown disturbance, an online disturbance estimation algorithm is proposed to estimate actual disturbance only based on the input/output (I/O) measurement data. Then, a distributed MFAILC method with disturbance compensation is developed such that consensus tracking errors are convergent. Last, the effectiveness of the developed method can be illustrated from the simulation examples.

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