Deadzone-Modified Robust Adaptive Learning Bipartite Consensus for Heterogeneous Nonlinear Multiagent Systems

Abstract

In this paper, the robust adaptive learning bipartite consensus problem for heterogeneous nonlinear multiagent systems with unknown control gains, external disturbances, and actuator constraints under signed directed graphs is investigated. A novel neural distributed protocol is proposed, whose key techniques lie in the introduction of deadzone-modified Lyapunov functions and integral Lyapunov functions into the command filtered backstepping design. The former enhances the robustness of the undertaken system by attenuating the impact of complex uncertainties and transforms the robustness problem into a convergence one by introducing the deadzones, while the latter efficiently tackles both the state-dependent control gain functions and the input saturation nonlinearities, simplifying the consensus design significantly. In addition, the requirement of the command filtered design for the control gain functions is relaxed by the appropriate system transformation. Furthermore, the incremental adaptive algorithm takes the place of the integral adaptation for parameter learning, avoiding numerical integration in implementation. The theoretical results of the performance analysis are presented in detail, in which the boundedness of all closed-loop variables is examined, and the asymptotic consensus is achieved, in the sense that the bipartite synchronization error converges to a pre-specified region asymptotically. Numerical results verify the feasibility of the presented scheme. Note to Practitioners—This paper is devoted to the problem of robust adaptive learning bipartite consensus for heterogeneous nonlinear multiagent systems (MASs). It is important to investigate heterogeneous MASs in practical engineering applications, and a typical example is the air-ground cooperative combat system consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In addition, the bipartite consensus allows MASs to accomplish more diverse tasks. However, uncertain nonlinearities, external disturbances, and actuator constraints are widespread in system dynamics. Moreover, the unknown control gains make consensus design challenging. These issues are well addressed by adopting the key techniques including deadzone-modified strategy, integral Lyapunov synthesis and incremental adaptive learning mechanism. Furthermore, the proposed scheme guarantees the asymptotic convergence of the bipartite synchronization errors with a pre-specified accuracy, and enhances the system robustness under complex heterogeneous nonlinearities. Therefore, the presented method contributes to practical applications.