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 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.