Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive...

This paper proposes a novel dynamic event-triggered approximate optimal consensus control scheme based on adaptive dynamic programming (ADP) for nonlinear multi-agent systems (MASs) with unknown dynamics, aiming to bridge the gap between theoretical control design and practical physical system applications. Firstly, a neural network (NN) state observer is developed to address the common challenge in physical systems where direct measurement of key states is either costly or technically infeasible due to sensor limitations. Typical examples include joint velocities of manipulators and angular positions of unmanned aerial vehicles. To enhance robustness against real-world disturbances, a disturbance-aware term is incorporated into the cost function, ensuring the scheme's adaptability to complex operating environments of physical systems. Secondly, a dynamic event-triggered mechanism (DETM) is integrated to significantly reduce communication and computational overhead. This reduction is critical for resource-constrained physical systems; a representative example is distributed robotic arms. Meanwhile, the DETM rigorously eliminates Zeno behavior to guarantee practical implementability. Additionally, a critic-only NN architecture is designed to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which not only relaxes the restrictive persistent excitation (PE) condition but also reduces network complexity and computational load, making it suitable for real-time control of physical systems with limited on-board computing resources. Finally, the effectiveness and practicality of the proposed scheme are validated through two physics-relevant case studies: a nonlinear affine system mimicking industrial process dynamics and a multiple manipulator system. Simulation results demonstrate that the scheme achieves stable consensus tracking, robust disturbance rejection, and efficient resource utilization, providing a control solution for MASs.