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Neural Adaptive Fixed-time Consensus Tracking for Multiple Euler-Lagrange Systems with Quantized Inputs

He Li, Cheng-Lin Liu*, and Yu-Ling Li
International Journal of Control, Automation, and Systems, vol. 20, no. 9, pp.3075-3087, 2022

Abstract : In this article, the fixed-time consensus tracking problem is investigated for quantized multiple EulerLagrange systems (MELSs) with state constraints. First, a fixed-time distributed observer is constructed to estimate the leader’s states. Next, the barrier Lyapunov functions (BLFs) are designed to implement the state constraints, and the radial basis function neural networks (RBFNNs) are employed to approximate uncertain dynamics. Then, the adaptive fixed-time local control protocol is designed to ensure that each agent to track the leader’s states estimated by the observer. Besides, a hysteretic quantizer is used for the input signals to avoid the chattering phenomenon. With the Lyapunov stability theory, it is proved that all error signals are semi-global fixed-time stable. Eventually, a numerical example is presented to show the validity of the proposed method.

Keyword : "Adaptive neural networks (NNs) control, distributed observer, fixed-time consensus tracking, multiple Euler-Lagrange systems (MELSs), quantized inputs, state constraints. "

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