This article is concerned with fixed-time leader-follower consensus problem for multiagent systems (MASs) with output constraints, unknown control direction, unknown system dynamics, unknown external disturbance, and dead-zone control input. First, a fixed-time distributed observer is presented for each follower to estimate the leader's states. Next, using a modified nonlinear mapping, an output-constrained system is transformed into an unconstrained system. Then, by adopting adding a power integrator technique, radial basis function neural network (RBFNN) approximation, and adaptive method, the ideal fixed-time stable virtual control protocol is derived and the issues of unknown control direction, unknown system dynamics, and unknown external disturbance are addressed. Finally, the actual control protocol is developed using the bound of dead-zone parameters. It is shown that the proposed control scheme achieves fixed-time leader-follower consensus of the studied MAS. The presented control protocol is applied to the leader-follower consensus of inverted pendulums and simulation results verify its effectiveness.

中文翻译：

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具有约束和干扰的多智能体系统的自适应神经网络固定时间领导__ollower共识


本文关注具有输出约束、未知控制方向、未知系统动力学、未知外部干扰和死区控制输入的多智能体系统 (MAS) 的固定时间领导者-跟随者共识问题。首先，为每个追随者提供一个固定时间的分布式观察者来估计领导者的状态。接下来，使用改进的非线性映射，将输出约束系统转换为无约束系统。然后，采用添加功率积分器技术、径向基函数神经网络（RBFNN）近似和自适应方法，推导了理想的固定时间稳定虚拟控制协议，解决了控制方向未知、系统动力学未知和外部未知的问题。干扰得到解决。最后，使用死区参数的界限开发实际的控制协议。结果表明，所提出的控制方案实现了所研究的 MAS 的固定时间领导者-跟随者共识。所提出的控制协议应用于倒立摆的主从一致性，仿真结果验证了其有效性。