A Measurement Approach to Validate the Predicted Behavior of a Nonlinear Mechanical Energy Harvester

We consider the response of a nonlinear vibration energy harvester, realized by a bistable snap-through buckling beam. A theoretical model for the system has, previously, been shown to reproduce the experimentally observed behavior, however there are parameter regimes when the model-system agreement is not good. Here we apply a slightly modified model to the same system. We also develop a Neuro- Fuzzy model that can be "trained" to reproduce the observed dynamical behavior. With this NF model, we can obtain the system response that more closely (than the theoretical model) matches the observed behavior. The NF model showed a performance of 99% and 87% during the training and test phases, respectively. Performance of the model was evaluated by a suitable performance index. This affords us a route towards predicting, more accurately, the experimental behavior over a wider range of parameters; this is important in our quest to optimize the system response and energy harvesting efficiency over a wider operating range.