This article presents a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this article, a data-driven minimum entropy design is presented without preknowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.

中文翻译：

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使用累积量生成函数的随机非线性系统的数据驱动最小熵控制

本文针对一类受非高斯噪声影响的随机非线性系统提出了一种新颖的最小熵控制算法。熵控制可以被认为是系统随机性衰减的优化问题，但均值必须单独考虑。为了克服这一缺点，在矩生成函数的基础上，利用累积量生成函数给出了系统随机特性的一种新的表示形式，其中平均值和熵通过累积量生成函数的形状来反映。基于系统输出和控制输入的样本，识别时变线性模型，并将最小熵优化转化为系统稳定。然后，开发了最优控制策略来实现随机性衰减，并分析了受控系统输出的有界性。通过数值例子证明了所提出的控制算法的有效性。本文提出了一种数据驱动的最小熵设计，无需预先了解系统模型；熵优化是通过系统稳定方法实现的，其中随机分布控制和最小熵使用相同的识别结构统一；由于可以采用所有现有的系统稳定方法来实现最小熵目标，因此获得了潜在的框架。在不预先了解系统模型的情况下提出数据驱动的最小熵设计；熵优化是通过系统稳定方法实现的，其中随机分布控制和最小熵使用相同的识别结构统一；由于可以采用所有现有的系统稳定方法来实现最小熵目标，因此获得了潜在的框架。在不预先了解系统模型的情况下提出数据驱动的最小熵设计；熵优化是通过系统稳定方法实现的，其中随机分布控制和最小熵使用相同的识别结构统一；由于可以采用所有现有的系统稳定方法来实现最小熵目标，因此获得了潜在的框架。