The neural-network (NN)-based output-feedback control is considered for a class of stochastic nonlinear systems under round-Robin (RR) scheduling protocols. For the purpose of effectively mitigating data congestions and saving energies, the RR protocols are implemented and the resulting nonlinear systems become the so-called protocol-induced periodic ones. Taking such a periodic characteristic into account, an NN-based observer is first proposed to reconstruct the system states where a novel adaptive tuning law on NN weights is adopted to cater to the requirement of performance analysis. In addition, with the established boundedness of the periodic systems in the mean-square sense, the desired observer gain is obtained by solving a set of matrix inequalities. Then, an actor_critic NN scheme with a time-varying step length in adaptive law is developed to handle the considered control problem with terminal constraints over finite-horizon. Some sufficient conditions are derived to guarantee the boundedness of estimation errors of critic and actor NN weights. In view of these conditions, some key parameters in adaptive tuning laws are easily determined via elementary algebraic operations. Furthermore, the stability in the mean-square sense is investigated for the discussed issue in infinite horizon. Finally, a simulation example is utilized to illustrate the applicability of the proposed control scheme.

中文翻译：

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循环调度协议下基于神经网络的输出反馈控制


基于神经网络（NN）的输出反馈控制被考虑用于循环（RR）调度协议下的一类随机非线性系统。为了有效缓解数据拥塞和节省能源，实现了RR协议，由此产生的非线性系统成为所谓的协议诱导周期性系统。考虑到这种周期性特征，首先提出了基于神经网络的观测器来重建系统状态，其中采用了神经网络权重的新型自适应调整律来满足性能分析的要求。此外，随着周期系统在均方意义上的有界性的建立，期望的观察者增益可以通过求解一组矩阵不等式来获得。然后，开发了自适应律中具有时变步长的 actor_critic NN 方案，以处理所考虑的有限范围内具有终端约束的控制问题。导出了一些充分条件来保证批评者和行动者神经网络权重估计误差的有界性。鉴于这些条件，自适应调谐定律中的一些关键参数可以通过初等代数运算轻松确定。此外，针对无限范围内讨论的问题，研究了均方意义上的稳定性。最后，利用仿真例子说明了所提出的控制方案的适用性。