A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input–output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.

中文翻译：

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具有传感器故障的未知 MIMO 非线性离散时间多智能体系统的数据驱动编队控制

针对具有传感器故障的未知异构非仿射非线性离散时间 MIMO 多智能体系统 (MAS)，提出了一种数据驱动的分布式编队控制算法。对于所考虑的未知MAS，利用无模型自适应控制(MFAC)理论中的动态线性化技术，将未知MAS转化为等效的虚拟动态线性化数据模型。然后利用虚拟数据模型构建了分布式无模型自适应控制器的结构。对于由于传感器故障导致的不正确信号测量，首先在无故障情况下为 MAS 训练径向基函数神经网络 (RBFNN)，然后使用训练有素的 RBFNN 的输出和在无故障情况下 MAS 的实际输出传感器故障案例，虚拟数据模型中未知故障和系统参数的估计规律仅使用测量的输入输出 (I/O) 数据信息进行设计。最后，通过收缩映射法和数学归纳法分析了地层误差的有界性。通过仿真实例说明了所提算法的有效性。