Observer-based adaptive neural consensus control of nonlinear multi-agent systems under input and output quantization

Abstract

In this article, for a series of nonlinear multi-agent systems under input and output quantization, a novel observer-based adaptive neural leader-following consensus control strategy is raised. Different from the existing output feedback consensus control strategies, in this raised strategy, the output and input of the agent are communicated through a directed network and quantized before communication. First of all, according to the quantized input and output information, a neural networks (NNs)-based distributed state observer is built by using the NNs to approximate the unknown functions. Secondly, in the backstepping process, the partial derivatives of the virtual control signals are non-existent because of the quantized output’s discontinuity. To avoid this issue, a command filtering technique is applied. Moreover, by constructing an intermediate auxiliary control signal, an actual adaptive consensus controller is designed. Thirdly, to compensate for the impact of quantization errors, Lemma 3 is presented. On this basis, the raised strategy guarantees that all signals of the closed-loop system are semi-globally bounded and the followers’ outputs converge to a neighborhood of the output of the leader. Lastly, two examples are applied to demonstrate the feasibility of this strategy.