ArticleScientific reports2024
Real-time deep learning-based model predictive control of a 3-DOF biped robot leg.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Robust and intelligent control strategies for a 3-DOF robotic arm: a comparative study.Scientific reports · 2026Article
- Real-time decentralized model predictive control for cooperative multi-robot object transport: experimental validation.Scientific reports · 2026Article
- AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning.Scientific reports · 2025Article
- The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.