Adaptive Neural Control for Novel Constrained Nonlinear Nonstrict Feedback Mixed MASs via Command Filter
摘要
In this paper, dynamic surface control (DSC) with radial basis function neural networks (RBFNNs) is addressed for the novel nonlinear mixed multiagent systems with constraints. Each agent can be a nonstrict state or output feedback system. By using the nonlinear transformation rules (NTRs), the states or output constraints can be handled. By using the compensating signals, the filtering errors can be eliminated. The stability analysis demonstrates that all the signals are bounded.