Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System
Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/382 |
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| author | Boyu Shu Zhiwu Huang Wanwan Ren Yue Wu Heng Li |
| author_facet | Boyu Shu Zhiwu Huang Wanwan Ren Yue Wu Heng Li |
| author_sort | Boyu Shu |
| collection | DOAJ |
| description | Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery–supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. Secondly, a multi-objective optimization objective function is established for the MPC-based energy management strategy. Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. Hardware-in-the-loop experimental implementations demonstrate that the proposed method can improve the kinematic performance of the legged robot by maintaining the supercapacitor state of charge at a relatively high level and reducing the battery capacity loss by 12.7% compared with the conventional MPC method. |
| format | Article |
| id | doaj-art-282df7ea4883422985ca32260de4c733 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-282df7ea4883422985ca32260de4c7332025-01-10T13:15:22ZengMDPI AGApplied Sciences2076-34172025-01-0115138210.3390/app15010382Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage SystemBoyu Shu0Zhiwu Huang1Wanwan Ren2Yue Wu3Heng Li4School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410075, ChinaSchool of Electronic Information, Central South University, Changsha 410075, ChinaSchool of Electronic Information, Central South University, Changsha 410075, ChinaElectrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery–supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. Secondly, a multi-objective optimization objective function is established for the MPC-based energy management strategy. Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. Hardware-in-the-loop experimental implementations demonstrate that the proposed method can improve the kinematic performance of the legged robot by maintaining the supercapacitor state of charge at a relatively high level and reducing the battery capacity loss by 12.7% compared with the conventional MPC method.https://www.mdpi.com/2076-3417/15/1/382energy management strategyelectrically driven legged robothybrid energy storage systemlearning-based model predictive control |
| spellingShingle | Boyu Shu Zhiwu Huang Wanwan Ren Yue Wu Heng Li Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System Applied Sciences energy management strategy electrically driven legged robot hybrid energy storage system learning-based model predictive control |
| title | Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System |
| title_full | Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System |
| title_fullStr | Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System |
| title_full_unstemmed | Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System |
| title_short | Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System |
| title_sort | learning based model predictive control for legged robots with battery supercapacitor hybrid energy storage system |
| topic | energy management strategy electrically driven legged robot hybrid energy storage system learning-based model predictive control |
| url | https://www.mdpi.com/2076-3417/15/1/382 |
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