State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach

A state consensus cooperative adaptive dynamic programming (ADP) control strategy is proposed for a nonlinear multi-agent system (MAS) with output constraints. On the basis of the transformation function, state models of leader and followers are transformed into affine ones. By using a monotonically increasing mapping function, the state-consensus cooperative control problem for an MAS with output constraints is equivalently transformed into a cooperative approximately optimal control one for an affine MAS. Then, a neural network observer is constructed for estimation of inner states, and, by graph theory and ADP method, the state consensus cooperative ADP control strategy is developed. The proposed strategy guarantees the performance index of the transformed system is approximately optimal. Furthermore, the stability analysis of whole closed-loop system is presented. Through the Lyapunov Theorem, we prove that the states of the MAS achieve consensus and the output signals of the followers satisfy the constraints. Also, all signals of the closed-loop MAS are bounded, and the trajectory of the leader node is cooperative bounded. The theoretical analysis and effectiveness of the strategy are verified by both a physical and a numerical example.