Neural network-based adaptive fault tolerant consensus control for a class of high order multiagent systems with input quantization and time-varying parameters

This paper studies the adaptive leader-following consensus control for a class of strict-feedback multi-agent systems. All the agents possess the quantized inputs, the time-varying unknown parameters and the actuator failures. By estimating the upper bounds of the induced uncertainties, the obstacles caused by discontinuous input quantization can be circumvented. Meanwhile, several distributed adaptive laws are established such that the coupled uncertainties caused by the actuator faults and the time-varying unknown parameters can be handled. Since the desired trajectory is only partly known, an adaptive compensating term is introduced in the control structure. Moreover, to deal with the completely unknown nonlinear functions, radial basis function neural networks (RBFNNs) are introduced for approximation and compensation. It is shown that the output consensus can be achieved and the boundedness of all the signals can be guaranteed. Finally, we show the efficacy of our theoretical results using a numerical example.