Optimal Tracking Control for Leader-Following Consensus of Nonlinear Multiagent Systems

This article investigates distributed adaptive leader-following consensus tracking optimal control problem for nonlinear multiagent systems (MASs) subject to unknown nonlinearities and uncertain external disturbances. In contrast to traditional centralized control, the primary challenge is that only partial subsystems can access the desired reference trajectory. To address this, positive time-varying smooth function compensating terms are introduced to counteract the effects of uncertain external disturbances and unknown desired trajectories. Then, by fusing consensus errors into the backstepping technique, feedforward controllers are given. On this basis, the controlled nonlinear systems are transformed into an equivalent affine form, and feedback optimal controllers are designed using actor and critic neural networks (NNs) to execute control behavior and evaluate control performance. The whole control laws comprise both feedforward and feedback controllers. The proposed distributed adaptive consensus control protocol can simultaneously achieve desired optimal control performance and minimize the cost function, as demonstrated through theoretical analysis and simulation results.