Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

This paper highlights the utilization of parallel control and adaptive dynamic programming (ADP) for event-triggered robust parallel optimal consensus control (ETRPOC) of uncertain nonlinear continuous-time multiagent systems (MASs). First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian., allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique”s introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then., an event-triggered mechanism is adopted to save communication resources while ensuring the system”s stability. The coupled Hamilton- Jacobi (HJ) equation”s solution is approximated using a critic neural network (NN)., whose weights are updated in response to events. Furthermore., theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded (UUB). Finally., numerical simulations demonstrate the effectiveness of the developed ETRPOC method.