Beyond Wave Variables: A Data-Driven Ensemble Approach for Enhanced Teleoperation Transparency and Stability

2025-12-09

AI Summary

To address degraded transparency and compromised stability in bilateral teleoperation systems induced by communication time delays, this paper proposes a data-driven integrated modeling framework that replaces conventional wave-variable transformations. Methodologically, it innovatively combines Optuna-optimized LSTM-Prophet for trend correction, CNN-LSTM for local dynamic modeling, and clustering-enhanced random forests, integrated via a stacked meta-learner to enable multi-model synergy; passivity constraints are embedded throughout the pipeline to guarantee closed-loop stability. Evaluated on MATLAB/Simulink-simulated data under varying time delays and noise conditions, the proposed approach achieves transparency comparable to baseline methods while significantly suppressing wave reflections and noise interference. Experimental results demonstrate superior robustness and practical engineering applicability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Machine Learning: Time-Series/Data Streams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Enhances teleoperation transparency and stability under time delays
• Replaces wave variables with data-driven ensemble models
• Ensures passivity for stability while handling delays and noise

Innovation

• Data-driven ensemble replaces wave-variable transform
• Three sequence models optimized via Optuna optimizer
• Stacking meta-learner combines models for transparency and stability