Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems
研究了存在对抗性交互和未知迟滞的非线性网络系统的二分一致性跟踪控制问题，提出自适应神经控制协议，确保信号有界并实现二分一致性。
This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.