Fuzzy Adaptive Learning Bipartite Consensus for Strict-Feedback Structurally Unbalanced Multiagent Systems With State Constraints

In this article, the fuzzy adaptive learning bipartite consensus problem is addressed for strict-feedback multiagent systems subject to state constraints under a structurally unbalanced signed graph. An agent hierarchical categorization strategy is suggested, with the assistance of which the requirements on the network topology can be relaxed, and the bipartition of all agents is easily achieved, even if the communication graph is structurally unbalanced. In addition, taking advantage of the treatment with symmetric fractional barrier Lyapunov functions, which transforms asymmetric constrained scenarios into symmetric cases and subsequently into equivalent unconstrained ones, it facilitates the realization of the bipartite consensus under state constraints and the performance analysis is greatly simplified. Furthermore, the fuzzy logic systems are employed to approximate the uncertainties involved in the system. It is shown that the boundedness of all variables of the closed-loop system undertaken and the convergence of consensus errors are established, even for the structurally unbalanced topology graph. Numerical results demonstrate feasibility of the presented consensus scheme.