The MegaRoller project, funded under the European Union’s Horizon 2020 research and innovation programme, aims to develop and demonstrate a novel Power Take-Off (PTO) solution for Wave Energy Converters (WECs). As part of the project, a wave-by-wave prediction software was developed, with a neural network prediction algorithm at its core. In this paper, the impact of the control strategy on key metrics is considered, focusing on assessing the potential of such wave-by-wave prediction software in improving the power performance and survivability of the system. In particular, two applications are considered: in a first step, a wave-by-wave damping adjustment control strategy, aiming at maximising the power capture, is compared to a baseline control strategy. When considering the additional complexity of the control system, the limited gains in power production suggest that, for the MegaRoller device, wave-by-wave damping control may not be beneficial enough. In a second step, methods for utilising the twin drive-trains of the MegaRoller device to counteract undesirable torque loads on the bearings in cases of oblique waves are investigated, comparing a baseline case to the application of an asymmetrical force in the PTO cylinders, adjusted either on a sea state by sea state, or a wave-by-wave basis. Such approach is shown to significantly improve the system’s survivability, reducing torque loads on the bearings. The impact of error on the wave-by-wave prediction is also shown to have a minimal impact on the metrics considered, providing confidence in the suitability of the prediction tool developed for the proposed purpose.