Finite-horizon robust formation-containment control of multi-agent networks with unknown dynamics

In the paper, data-driven finite-horizon robust formation-containment control scheme is developed based on integral reinforcement learning and zero-sum game for perturbed multi-agent networks with completely unknown nonlinear dynamics. At first, distributed finite-time sliding mode estimators are designed to obtain the desired states of leaders and followers, respectively. Then finite-horizon robust leader formation control and follower containment control are achieved based on proposed model-free integral reinforcement learning algorithms implemented by critic-actor-disturbance structure, in the framework of multi-player zero-sum game where the non-quadratic performance index for each agent considers the influence of saturated inputs and disturbances of local neighbors thoroughly. Furthermore, it is proved that the whole network has bounded L₂ gain robust stability and Nash equilibrium of zero-sum game exists. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.