Dynamic event-triggered data-driven iterative learning consensus control for nonlinear MASs with unknown disturbance

This work tackles the consensus problem for nonlinear nonaffine multi-agent systems (MASs) with unknown disturbances using a data-driven iterative learning control (DDILC) scheme. The unknown model of the agent is first converted through dynamic linearisation into a equivalent data model, comprising a linear combination of an unknown dynamic parameter and an uncertain term. An adaptive algorithm is then formulated to estimate the unknown dynamic parameters accurately. The total uncertainty, which arises from the parameter estimation error and the uncertain term, is estimated by the extended state observer. The estimations are used by the controller to compensate for total uncertainty. Further, considering the resource limitations of communication and the constraints of the actual control input, a constrained iterative learning controller with a dynamic event-triggered mechanism is developed, which guarantees control performance and reduces the number of communications in MASs. It is proven that the tracking error asymptotically approaches a small bound around zero under the proposed DDILC framework. The simulation results confirm the effectiveness of the theoretical research.