This article investigates the quantized adaptive finite-time bipartite tracking control problem for high-order stochastic pure-feedback nonlinear multiagent systems with sensor faults and Prandtl–Ishlinskii (PI) hysteresis. Different from the existing finite-time control results, the nonlinearity of each agent is totally unknown in this article. To overcome the difficulties caused by asymmetric hysteresis quantization and PI hysteresis, a new distributed control method is proposed by adopting the adaptive compensation technique without estimating the lower bounds of parameters. Radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback nonlinear systems. Then, an adaptive neural-network compensation control approach is proposed to tackle the problem of sensor faults. The problem of the “explosion of complexity” caused by repeated differentiations of the virtual controller is solved by using the dynamic surface control technique. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobal practical finite-time stable in probability, and the bipartite tracking control performance is achieved. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

中文翻译：

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随机多智能体系统的量化自适应有限时双向NN跟踪控制

本文研究了具有传感器故障和Prandtl–Ishlinskii（PI）磁滞现象的高阶随机纯反馈非线性多主体系统的量化自适应有限时间双向跟踪控制问题。与现有的有限时间控制结果不同，本文不了解每种代理的非线性。为了克服非对称磁滞量化和PI磁滞带来的困难，提出了一种新的分布式控制方法，该方法采用自适应补偿技术，而无需估计参数的下限。利用径向基函数神经网络来估计未知的非线性函数，并解决由纯反馈非线性系统引起的代数环问题。然后，提出了一种自适应神经网络补偿控制方法来解决传感器故障的问题。通过使用动态表面控制技术解决了由虚拟控制器的反复微分引起的“复杂性爆炸”问题。基于Lyapunov稳定性定理，证明了闭环系统的所有信号在概率上都是半全局的有限时稳定的，并实现了二部跟踪控制性能。最后，通过仿真结果验证了所提出控制策略的有效性。实践证明，该闭环系统的所有信号在概率上都是半全局的有限时稳定的，并实现了双向跟踪控制性能。最后，通过仿真结果验证了所提出控制策略的有效性。实践证明，该闭环系统的所有信号在概率上都是半全局的有限时稳定的，并实现了双向跟踪控制性能。最后，通过仿真结果验证了所提出控制策略的有效性。