This article investigates the leader‐follower consensus problem of a class of non‐strict‐feedback nonlinear multiagent systems with asymmetric time‐varying state constraints (ATVSC) and input saturation, and an adaptive neural control scheme is developed. By introducing the distributed sliding‐mode estimator, each follower can obtain the estimation of leader's trajectory and track it directly. Then, with the help of time‐varying asymmetric barrier Lyapunov function and radial basis function neural networks, the controller is designed based on backstepping technique. Furthermore, the mean‐value theorem and Nussbaum function are utilized to address the problems of input saturation and unknown control direction. Moreover, the number of adaptive laws is equal to that of the followers, which reduces the computational complexity. It is proved that the leader‐follower consensus tracking control is achieved without violating the ATVSC, and all closed‐loop signals are semiglobally uniformly ultimately bounded. Finally, the simulation results are provided to verify the effectiveness of the control scheme.

中文翻译：

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具有非对称时变状态约束和输入饱和的多主体系统的自适应神经控制

本文研究了一类具有非对称时变状态约束（ATVSC）和输入饱和的非严格反馈非线性多主体系统的前导跟随共识问题，并提出了一种自适应神经控制方案。通过引入分布式滑模估计器，每个跟随者都可以获得领导者轨迹的估计并直接跟踪。然后，借助时变不对称障碍Lyapunov函数和径向基函数神经网络，基于反推技术设计了控制器。此外，均值定理和Nussbaum函数用于解决输入饱和和未知控制方向的问题。而且，自适应律的数量等于跟随者的数量，这降低了计算复杂度。事实证明，在没有违反ATVSC的情况下，可以实现对领导者的共识跟踪控制，并且所有闭环信号最终都是半全局一致的。最后，提供仿真结果以验证控制方案的有效性。