In this article, a novel composite hierarchical antidisturbance control (CHADC) algorithm aided by the information-theoretic learning (ITL) technique is developed for non-Gaussian stochastic systems subject to dynamic disturbances. The whole control process consists of some time-domain intervals called batches. Within each batch, a CHADC scheme is applied to the system, where a disturbance observer (DO) is employed to estimate the dynamic disturbance and a composite control strategy integrating feedforward compensation and feedback control is adopted. The information-theoretic measure (entropy or information potential) is employed to quantify the randomness of the controlled system, based on which the gain matrices of DO and feedback controller are updated between two adjacent batches. In this way, the mean-square stability is guaranteed within each batch, and the system performance is improved along with the progress of batches. The proposed algorithm has enhanced disturbance rejection ability and good applicability to non-Gaussian noise environment, which contributes to extending CHADC theory to the general stochastic case. Finally, simulation examples are included to verify the effectiveness of theoretical results.

中文翻译：

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基于信息论学习技术的非高斯随机系统复合抗扰控制

在本文中，针对受动态干扰的非高斯随机系统，开发了一种由信息论学习 (ITL) 技术辅助的新型复合分层抗扰动控制 (CHADC) 算法。整个控制过程由一些称为批处理的时域间隔组成。在每个批次中，CHADC 方案应用于系统，其中采用扰动观测器 (DO) 来估计动态扰动，并采用集成前馈补偿和反馈控制的复合控制策略。采用信息论测度（熵或信息势）来量化受控系统的随机性，并据此在两个相邻批次之间更新 DO 和反馈控制器的增益矩阵。这样，每个batch内均方稳定性得到保证，系统性能随着batch的进步而提高。该算法具有增强的抗扰能力和对非高斯噪声环境的良好适用性，有助于将CHADC理论扩展到一般随机情况。最后给出仿真实例验证了理论结果的有效性。