A non-intrusive data-driven approach towards optimal control of electroactive polymers via dimensionality reduction
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In this work, we present a non-intrusive reduced order model (ROM) for the optimal control of Dielectric Elastomer Actuators (DEA), a type of electroactive polymer (EAP). DEAs are inherently coupled problems due to the interaction between electrical stimuli and mechanical deformation. The primary objective is to accelerate the computationally costly process of determining the optimal electrical stimuli required to achieve a specific shape in a dielectric elastomer body. Traditional methods for solving this problem[1] are computationally intensive and often infeasible for real-time applications. Our approach leverages model order reduction (MOR) techniques to develop a non-intrusive ROM that significantly reduces the computational burden. The ROM is constructed using a combination of kernel Principal Component Analysis (kPCA)[2] and Isometric Mapping (isomap)[3]. This method allows for efficient and accurate simulations, making it feasible to perform optimal control in a timely manner. We validate our approach through experiments, demonstrating the effectiveness of the ROM in capturing the complex behavior of DEAs under various electrical stimuli for several body configurations. The results show a substantial reduction in computational time while maintaining high accuracy, highlighting the potential of our method for real-time control applications.