Time delays in communication channels present significant challenges for bilateral teleoperation systems, affecting both transparency and stability. Although traditional wave variable-based methods for a four-channel architecture ensure stability via passivity, they remain vulnerable to wave reflections and disturbances like variable delays and environmental noise. This article presents a data-driven hybrid framework that replaces the conventional wave-variable transform with an ensemble of three advanced sequence models, each optimized separately via the state-of-the-art Optuna optimizer, and combined through a stacking meta-learner. The base predictors include an LSTM augmented with Prophet for trend correction, an LSTM-based feature extractor paired with clustering and a random forest for improved regression, and a CNN-LSTM model for localized and long-term dynamics. Experimental validation was performed in Python using data generated from the baseline system implemented in MATLAB/Simulink. The results show that our optimized ensemble achieves a transparency comparable to the baseline wave-variable system under varying delays and noise, while ensuring stability through passivity constraints.

翻译：通信信道中的时间延迟对双边遥操作系统构成显著挑战，影响系统的透明度与稳定性。尽管基于传统波变量的四通道架构方法通过无源性保证了稳定性，但仍易受波反射以及可变延迟和环境噪声等干扰的影响。本文提出一种数据驱动的混合框架，该框架用三个先进序列模型的集成替代了传统的波变量变换。每个模型均通过最先进的Optuna优化器单独优化，并通过堆叠元学习器进行组合。基础预测器包括：一个结合Prophet进行趋势校正的LSTM模型、一个基于LSTM的特征提取器与聚类及随机森林结合以改进回归的模型，以及一个用于捕捉局部与长期动态的CNN-LSTM模型。实验验证在Python环境中进行，使用了基于MATLAB/Simulink实现的基线系统生成的数据。结果表明，在可变延迟和噪声条件下，我们优化的集成方法在透明度方面达到了与基线波变量系统相当的水平，同时通过无源性约束确保了稳定性。