Cyclic-Small-Gain Approach to Adaptive Control for Multiagent Systems With Unknown Interconnected Dynamics

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.