This article addresses the optimal control problem for nonlinear multiagent systems (MASs) with an uncertain nonlinear leader subject to intermittent Denial-of-Service (DoS) attacks. The main challenge is estimating the leader's dynamics when the uncertain nonlinear dynamics of the leader are unknown to all followers and communication between subsystems is intermittently disrupted by attacks. Furthermore, the uncertainty in the followers' dynamics adds complexity, making it difficult to eliminate reliance on the identifier network. To overcome these challenges, we develop a learning-based adaptive distributed observer to estimate the leader's dynamics under attacks. Based on this observer, a single-critic optimal consensus tracking control scheme is proposed to solve the leader-follower consensus problem in uncertain MASs without requiring an identifier network. It is proven that all system signals are uniformly ultimately bounded (UUB), and consensus tracking is achieved. The effectiveness of the proposed method is validated through a simulation example.

This study addresses the learning-based resilient adaptive fuzzy optimal consensus control problem for nonlinear uncertain Multiagent Systems (MASs) in the presence of intermittent Denial of Service (DoS) attacks. A key obstacle is the uncertainty in the dynamics of the followers, which makes it challenging to eliminate dependency on the identifier network. To this end, we propose a novel critic-only optimal consensus scheme to eliminate dependency on the identifier network and significantly reduce computational complexity. Moreover, this work requires less prior knowledge and assumes that only the specific subsystems can access the leader's information under certain conditions. To cope with limited information access, we design a distributed adaptive observer to monitor the leader's dynamics. It is proven that all the signals are uniformly ultimately bounded(UUB), and consensus tracking is achieved. Finally, a simulation example is provided to demonstrate the results achieved.

Developing a distributed output feedback consensus tracking control scheme for nonlinear multiagent systems (MASs) with interconnected dynamics and unmeasurable states holds significant practical importance. Current approaches to this challenge often rely on fuzzy logic systems (FLSs) or neural networks (NNs) for direct compensation of interconnected terms. However, these methods frequently result in incomplete compensation, posing obstacles to achieving true distributed control. In this study, the cyclic-small-gain condition is utilized to address the challenge of unknown interconnected dynamics. This approach effectively decouples the physical coupling among MASs, enabling the realization of distributed control. Further, direct compensation using the cyclic-small-gain condition introduces the potential singularity problem, which we effectively overcome by employing the inequality technique. Based on the presented control scheme, the existing control results for the MASs with interconnected dynamics are extended from stabilization control to trajectory tracking. As proved, the synchronization and observer errors eventually converge to a tunable zero region, enabling each agent to effectively track the leader. Additionally, the proposed control scheme employs a direct adaptive law design approach with a significantly reduced number of adaptive laws. Simulation results validate the theoretical scheme.