For multi-input multi-output (MIMO) nonlinear discrete-time bipartite formation multiagent systems (BFMASs) performing trajectory tracking tasks with unknown dynamics, a dynamic event-triggered model-free adaptive iterative learning control (DET-MFAILC) algorithm is proposed to address periodic denial-of-service (DoS) attacks. First, using the pseudo-partial derivative, a compact format dynamic linearization (CFDL) method is employed to construct an equivalent CFDL data model for the MIMO multi-agent system. A DoS attack model and its corresponding compensation algorithm are developed, while a dynamic event-triggered condition is designed considering both the consensus error and the tracking error. Subsequently, the proposed DoS attack compensation algorithm and the dynamic event-triggered mechanism are integrated with the model-free adaptive iterative learning control algorithm to design a controller, which is further extended from fixed-topology systems to time-varying topology systems. The convergence of the control system is rigorously proven. Finally, simulation experiments are conducted on bipartite formation multi-agent systems (BFMASs) under fixed and time-varying communication topologies. The results demonstrate that the proposed algorithm effectively mitigates the impact of DoS attacks, reduces controller updates, conserves network resources, and ensures that both the tracking error and consensus error converge to an ideal range close to zero within a finite number of iterations while maintaining a good formation shape.

The leader-follower consensus tracking control and containment control problems for nonlinear nonaffine multi input multi output (MIMO) multi-agent systems without a leader or with a leader-follower are investigated. Different from the conventional controller based compact form dynamic linearization (CFDL) method for this problem, a new distributed model free adaptive control method (CFFDL-DMFAC) is proposed by using the controller based full form dynamic linearization (CFFDL) method and a new time-varying pseudo Jacobian matrix. This method does not require a mathematical model of the system during the controller design process, and only uses real time I/O data between neighboring agents to achieve the common goal of consensus tracking of each agent. At the same time, the use of consensus error dynamic linearization methods enables the algorithm design and stability analysis of containment control to be analyzed in a unified manner with leaderless and leader-follower consensus control. Finally, rigorous mathematical proofs and simulation results are used to verify the convergence and effectiveness of the proposed control scheme.