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Ionic polymer–metal composite (IPMC) actuators are one of the most prominent electroactive polymers with expected widespread use in the future. The IPMC bends in response to a small applied electric field as a result of the mobility of cations in the polymer network. This paper proposes a Levenberg–Marquardt algorithm backpropagation neural network (LMA–BPNN) prediction model applicable for Cu/Nafion-based ionic polymer–metal composites to predict the actuation property. The proposed approach takes the dimension ratio (DR) and stimulation voltage as the input layer, displacement and blocking force as the output layer, and trains the LMA–BPNN with the experimental data so as to obtain a mapping relationship between the input and the output and obtain the predicted values of displacement and blocking force. An IPMC actuating system is set up to generate a collection of the IPMC actuating data. Based on the input/output training data, the most suitable structure was found out for the BPNN model to represent the IPMC actuation behavior. After training and verification, a 2-9-3-1 BPNN structure for displacement and a 2-9-4-1 BPNN structure for blocking force indicate that the structure can provide a good reference value for the IPMC. The results showed that the BPNN model based on the LMA could predict the displacement and blocking force of the IPMC. Therefore, this model can become an effective solution for IPMC control applications.

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In this study, Cu-Ionic polymer metal composites (Cu-IPMC) were fabricated using the electroless plating method. The properties of Cu-IPMC in terms of morphology, water loss rate, adhesive force, surface resistance, displacements, and tip forces were evaluated under direct current voltage. In order to understand the relationship between lengths and actuation properties, we developed two static models of displacements and tip forces. The deposited Cu layer is uniform and smooth and contains about 90% by weight of copper, according to the energy-dispersive X-ray spectroscopy (EDS) analysis data obtained. The electrodes adhere well (level of 5B) on the membrane, to ensure a better conductivity and improve the actuation performance. The penetration depth of needle-like electrodes can reach up to around 70 μm, and the structure shows concise without complex branches, to speed up the actuation. Overall the maximum displacement increased as the voltage increased. The applied voltage for the maximum force output is 8–9 V. The root mean square error (RMSE) and determination coefficient (DC) of the displacement and force models are 1.66 and 1.23, 0.96 and 0.86, respectively.

Based on the biological characteristics of tulip, the low driving voltage and fast response of ionic polymer metal composite (IPMC), we analyzed the fabrication, morphology and performance of the platinum IPMC (Pt-IPMC) and selected the right IPMC for driving bionic tulip. The preparation and performance of IPMC was analyzed first in this paper such as blocking force, output displacement and bending angle of IPMC under the different directed current voltage (DC). The optimal IPMC sample size and driving voltage were selected based on tulip blooming angles and the strain energy density of IPMC, which completed the blooming process of bionic tulip. The feasibility of IPMC used in driving bionic field was fully proved in this paper, which laid a foundation for the application of IPMC in driving biomimetic biological robots.