Sampled-Data Fault-Tolerant Bipartite Formation Control With Fuzzy Neural Network for Nonlinear Continuous-Time MIMO Multiagent Systems

This article proposes a sampled-data fault-tolerant bipartite formation control (SD-FTBFC) algorithm that incorporates the sampling period and historical input and output data for nonlinear continuous-time multi-input-multioutput (MIMO) multiagent systems (MASs) subjected to unknown sensor faults. First, a sampled-data full-form dynamic linearization data model employing the principles of differential and integral mean value theorems is established to address the unknown nonlinearities of the continuous-time MASs. Afterward, a time-varying fault detection threshold is developed to ascertain the occurrence of sensor faults. Subsequently, the unknown sensor faults are approximated by the fuzzy neural network algorithm, and the uncertain parameters of MASs are tackled by the projection algorithm. A rigorous stability analysis for the proposed SD-FTBFC algorithm is thoroughly presented. Ultimately, the simulation outcomes utilizing multiple unmanned ground vehicles confirm the efficacy of the proposed SD-FTBFC algorithm.