Abstract
This paper mainly concentrates on the distributed tracking control problem for a class of uncertain nonlinear multi-agent systems with quantized input signal. Unlike the previous results on quantized control for multi-agent systems, the nonlinearities in this research can be completely unknown. To cope with these unknown nonlinearities, adaptive neural networks technique is employed to approximate the unknown nonlinear functions. Meanwhile, backstepping approach is utilized to handle the control design issue. Based on an important property of nonlinear decomposition for quantizer, a distributed quantized feedback tracking control scheme is successfully proposed to ensure the stability of the whole systems and the implementation of synchronous tracking. Finally, a simulation example is used to illustrate the efficacy of the proposed control scheme.
Highlights
Quantized feedback control issue has received widespread attention in the last decades due to its practicality in the applications
Quantized feedback control has been gradually extended to multi-agent systems (MASs) in view of the actual
Compared with the existing results on adaptive quantized control for MASs, the main contributions of this dissertation lie in that 1) a backstepping based adaptive neural quantized control protocol has been developed, which solves the problem of consensus tracking control of high-order nonlinear uncertain MASs with quantized input signal
Summary
Quantized feedback control issue has received widespread attention in the last decades due to its practicality in the applications. Different from the existing results on quantization consensus control of MASs, each agent in the considered system is an n-order nonlinear system instead of second-order systems in [42] and [43]. Compared with the existing results on adaptive quantized control for MASs, the main contributions of this dissertation lie in that 1) a backstepping based adaptive neural quantized control protocol has been developed, which solves the problem of consensus tracking control of high-order nonlinear uncertain MASs with quantized input signal. The aim of this work is to develop an adaptive neural control strategy for the uncertain nonlinear MAS (1) with quantized input signal to make sure that the MAS asymptotically reaches consensus and all signals in this system are bounded
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