Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers
Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/6/1206 |
| _version_ | 1797592560168861696 |
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| author | Yuriy Kozhubaev Elena Ovchinnikova Ivanov Viacheslav Svetlana Krotova |
| author_facet | Yuriy Kozhubaev Elena Ovchinnikova Ivanov Viacheslav Svetlana Krotova |
| author_sort | Yuriy Kozhubaev |
| collection | DOAJ |
| description | Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medical technology is the development of suitable modeling and control approaches. Due to the complex non-linear behavior of soft materials and the unpredictable motion of actuators, the task of modeling complex soft actuators is very time-consuming. As a result, earlier studies have undertaken research into model-free methods for controlling soft actuators. In recent years, machine learning (ML) methods have become widely popular in research. The adaptability of an ML model to a non-linear soft drive system alongside the varying actuation behavior of soft drives over time as a result of material characteristics and performance requirements is the key rationale for including an ML model. The system requires the online updating of the ML model in order to work with the non-linear system. Sequential data collected from the test bench and converted into a hypothesis are used to perform incremental learning. These methods are called lifelong learning and progressive learning. Real-time data flow training is combined with incremental learning (IL), and a neural network model is tuned sequentially for each data input. In this article, a method for the intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed. A soft pneumatic actuator was subjected to three distinct test conditions in a controlled test environment for a specified duration of data gathering. Additionally, data were collected through finite element method simulations. The collected data were used to incrementally train a neural network, and the resulting model was analyzed for errors with both training and test data. The training and testing errors were compared for different incremental learning (IL) algorithms, including K-nearest neighbors, a decision tree, linear regression, and a neural network. The feasibility of the modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm was verified, solving the problem of the control of software actuators. |
| first_indexed | 2024-03-11T01:52:58Z |
| format | Article |
| id | doaj.art-47e6563fb2a94d20b50e7e520a1708fb |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-11T01:52:58Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-47e6563fb2a94d20b50e7e520a1708fb2023-11-18T12:50:56ZengMDPI AGSymmetry2073-89942023-06-01156120610.3390/sym15061206Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical ChambersYuriy Kozhubaev0Elena Ovchinnikova1Ivanov Viacheslav2Svetlana Krotova3Department of Informatics and Computer Technologies, St. Petersburg Mining University, St. Petersburg 199106, RussiaDepartment of Informatics and Computer Technologies, St. Petersburg Mining University, St. Petersburg 199106, RussiaDepartment of Informatics and Computer Technologies, St. Petersburg Mining University, St. Petersburg 199106, RussiaDepartment of Informatics and Computer Technologies, St. Petersburg Mining University, St. Petersburg 199106, RussiaSoft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medical technology is the development of suitable modeling and control approaches. Due to the complex non-linear behavior of soft materials and the unpredictable motion of actuators, the task of modeling complex soft actuators is very time-consuming. As a result, earlier studies have undertaken research into model-free methods for controlling soft actuators. In recent years, machine learning (ML) methods have become widely popular in research. The adaptability of an ML model to a non-linear soft drive system alongside the varying actuation behavior of soft drives over time as a result of material characteristics and performance requirements is the key rationale for including an ML model. The system requires the online updating of the ML model in order to work with the non-linear system. Sequential data collected from the test bench and converted into a hypothesis are used to perform incremental learning. These methods are called lifelong learning and progressive learning. Real-time data flow training is combined with incremental learning (IL), and a neural network model is tuned sequentially for each data input. In this article, a method for the intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed. A soft pneumatic actuator was subjected to three distinct test conditions in a controlled test environment for a specified duration of data gathering. Additionally, data were collected through finite element method simulations. The collected data were used to incrementally train a neural network, and the resulting model was analyzed for errors with both training and test data. The training and testing errors were compared for different incremental learning (IL) algorithms, including K-nearest neighbors, a decision tree, linear regression, and a neural network. The feasibility of the modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm was verified, solving the problem of the control of software actuators.https://www.mdpi.com/2073-8994/15/6/1206soft roboticsmachine learningmulti-objective regressionincremental learningneural networkpneumatic actuator |
| spellingShingle | Yuriy Kozhubaev Elena Ovchinnikova Ivanov Viacheslav Svetlana Krotova Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers Symmetry soft robotics machine learning multi-objective regression incremental learning neural network pneumatic actuator |
| title | Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers |
| title_full | Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers |
| title_fullStr | Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers |
| title_full_unstemmed | Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers |
| title_short | Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers |
| title_sort | incremental machine learning for soft pneumatic actuators with symmetrical chambers |
| topic | soft robotics machine learning multi-objective regression incremental learning neural network pneumatic actuator |
| url | https://www.mdpi.com/2073-8994/15/6/1206 |
| work_keys_str_mv | AT yuriykozhubaev incrementalmachinelearningforsoftpneumaticactuatorswithsymmetricalchambers AT elenaovchinnikova incrementalmachinelearningforsoftpneumaticactuatorswithsymmetricalchambers AT ivanovviacheslav incrementalmachinelearningforsoftpneumaticactuatorswithsymmetricalchambers AT svetlanakrotova incrementalmachinelearningforsoftpneumaticactuatorswithsymmetricalchambers |