Aiming at the tracking control problem of non-affine stochastic nonlinear systems with input saturation constraints, the event trigger control (ETC) based on Radial basic function neural networks (RBFNNs) and prescribed performance control (PPC) is considered. First, the mean value theorem is used to decouple the non-affine terms existing in the system. Second, the design process of PPC is reconstructed for a class of stochastic nonlinear systems, because the existence of stochastic disturbances has not been fully considered in previous literature on PPC, so that the system output is not to violate the set constraint bound by preset function. Finally, unlike the existing event triggering results, a special event triggering strategy is designed, which further takes into account the error between the preset function and the system output and the errors in all states of stochastic nonlinear systems, so it is expected that the amount of communications will be further reduced. Also, the proposed adaptive PPC scheme with event triggering mechanism can guarantee that all closed-loop signals are uniformly ultimately bounded (UUB) in probability within the appropriate compact sets. Finally, the effectiveness of the proposed method is verified by a practical example.

中文翻译：

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一类具有输入饱和约束的纯反馈随机非线性系统的事件触发自适应指定性能控制

针对具有输入饱和约束的非仿射随机非线性系统的跟踪控制问题，考虑了基于径向基本函数神经网络（RBFNNs）和规定性能控制（PPC）的事件触发控制（ETC）。首先，均值定理用于解耦系统中存在的非仿射项。其次，针对一类随机非线性系统重构了PPC的设计过程，因为以往关于PPC的文献没有充分考虑随机扰动的存在，使得系统输出不违反预设函数约束的集合约束. 最后，不同于现有的事件触发结果，设计了特殊的事件触发策略，进一步考虑了预置函数与系统输出之间的误差以及随机非线性系统所有状态的误差，因此预计通信量将进一步减少。此外，所提出的具有事件触发机制的自适应 PPC 方案可以保证所有闭环信号在适当的紧凑集合内的概率一致最终有界（UUB）。最后通过实例验证了所提方法的有效性。