TY - JOUR AB - Purpose The purpose of this paper is to investigate the neural-network-based containment control of multi-agent systems with unknown nonlinear dynamics. Moreover, communication constraints are taken into account to reflect more realistic communication networks.Design/methodology/approach Based on the approximation property of the radial basis function neural networks, the control protocol for each agent is designed, where all the information is exchanged in the form of sampled data instead of ideal continuous-time communications.Findings By utilizing the Lyapunov stability theory and the Lyapunov–Krasovskii functional approach, sufficient conditions are developed to guarantee that all the followers can converge to the convex hull spanned by the stationary leaders.Originality/value As ideal continuous-time communications of the multi-agent systems are very difficult or even unavailable to achieve, the neural-network-based containment control of nonlinear multi-agent systems is solved under communication constraints. More precisely, sampled-data information is exchanged, which is more applicable and practical in the real-world applications. VL - 36 IS - 2 SN - 0144-5154 DO - 10.1108/AA-11-2015-107 UR - https://doi.org/10.1108/AA-11-2015-107 AU - Ma Chao PY - 2016 Y1 - 2016/01/01 TI - Neural-network-based containment control of nonlinear multi-agent systems under communication constraints T2 - Assembly Automation PB - Emerald Group Publishing Limited SP - 179 EP - 185 Y2 - 2024/03/28 ER -