Bipartite containment control of multi-agent systems subject to adversarial inputs based on zero-sum game

In this paper, we investigate bipartite containment control problem of multi-agent systems (MASs) with signed directed graph under adversarial inputs. Firstly, we define the bipartite containment error and establish the equivalence between the bipartite containment error converging to zero and the achievement of bipartite containment control. Subsequently, we prove that the bounded L2 2-gain bipartite containment problem under adversarial inputs can be reformulated as a multi- player zero-sum differential graphical game problem and can be solved via the solution to the coupled Hamilton-Jacobi-Isaacs (HJI) equation. To address this, we propose a policy iteration (PI) algorithm and prove its convergence under different updating cases. The proposed algorithm is implemented by neural networks (NNs) and a numerical simulation example is provided to show its effectiveness.