Combined model-based and data-driven approach for the control of a soft robotic neck

This paper delves into the potential of integrating model-based and data-driven techniques for controlling the performance of a soft robotic neck. Artificial intelligence (AI) methods, such as machine learning and deep learning, have shown their applicability in modelling and controlling robotic sys...

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Veröffentlicht in:Robotics and autonomous systems Jg. 194; S. 105155
Hauptverfasser: Continelli, Nicole A., Nagua, Luis F., Olmos, Pablo M., Monje, Concepción A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2025
Schlagworte:
Data processing
Machine learning
Model-based control
Multi-layer perceptron
Neural network
Soft robotics
Soft robotics
Data processing
Neural network
Multi-layer perceptron
Model-based control
Machine learning
ISSN:0921-8890
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Zusammenfassung:This paper delves into the potential of integrating model-based and data-driven techniques for controlling the performance of a soft robotic neck. Artificial intelligence (AI) methods, such as machine learning and deep learning, have shown their applicability in modelling and controlling robotic systems with complex nonlinear behaviours. However, model-based approaches have also proven to be effective analytical alternatives, even if they rely on simplified approximations of the robot model. The control system proposed in this work combines the closed loop analytical model of the soft robotic neck with a Multi-Layer Perceptron (MLP) network trained to minimise the neck pose error. The MLP undergoes training with three different data treatments, and the results are compared to determine the most effective one. The experimental results obtained demonstrate the robustness of the proposed technique and its potential as an alternative to classical solutions, whether purely based on analytical models or data-driven models. •Hybrid control using the soft neck analytical model and MLP to minimise pose error.•MLP corrects model inaccuracies by predicting error in motor poses, adjusting the kinematic analytical model.•Data scaling is crucial; normalising by the MLP output distribution prevents overfitting.•Hybrid control system combines advantages of isolated approaches.•Integrate mathematical models and machine learning to enhance robustness.
ISSN:0921-8890
DOI:10.1016/j.robot.2025.105155