# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 1 Introduction
These methods show promise but require extensive training data and computational resources, limiting practical WEC deployment in real ocean environments. Despite control approaches from different angles, estimating incoming wave or sea state from the physical measurements of the WEC device remains limited. Despite these advancements, most WEC control strategies rely on direct wave measurements using buoys, radar, or pressure sensors to optimize power capture. But these approaches are costly, maintenance intensive, and vulnerable to harsh marine conditions. Real-time data acquisition also introduces latency and communication constraints, limiting its effectiveness for dynamic control. Moreover, wave sensors can fail or become impractical in extreme environments, making them unreliable for long-term deployment. These challenges highlight the need for an alternative approach that eliminates dependence on external wave measurements. Machine learning, particularly deep learning, offers a potential solution by extracting meaningful patterns from these onboard measurements. Neural networks have shown success in renewable energy applications [14], including wave prediction [16], but their use in WEC control remains underexplored. Furthermore, while nonlinear model-based controllers can handle system uncertainties, they require detailed parametric models and the extensive tuning of control parameters, which may be impractical under rapidly changing sea states. Reinforcement-learning-based tuning methods such as DDPG offer end-to-end policy optimization but demand large volumes of training data and online exploration, posing safety and sample-efficiency challenges in marine environments. To overcome these challenges, we propose a machine learning-based control strategy that estimates sea states directly from onboard WEC measurements (position, velocity, and force), eliminating the need for external wave sensors. In this study, we develop and evaluate a long short-term memory (LSTM)-based control framework that uses physical data from the WEC itself to predict the incoming wave spectrum, aiming to improve energy capture efficiency and device adaptability under real-world conditions. By training and validating the model across a broad range of operational scenarios, we demonstrate how this control strategy enhances WEC performance by making it more responsive and resilient to changing ocean waves. We propose a novel LSTM-based framework for real-time sea-state estimation using only onboard WEC measurements, and integrate this estimator into a supervisory control loop that updates PID gains every 10 s. Through comprehensive simulations, we demonstrate that this adaptive approach achieves a 25.6% increase in power capture under variable sea conditions relative to fixed-gain PI control. Furthermore, we show robustness to model mismatch by employing noise-augmented training during model development. This research contributes to advancing wave energy technology by addressing key limitations in current control strategies, potentially enabling more efficient and sustainable ocean energy solutions. This paper is structured as follows. Section 2 describes the WaveBot dynamics. Section 3 introduces our LSTM-based estimation framework and supervisory control. Section 4 details the PI-gain design and stability analysis. Section 5 presents the simulation results under fixed and variable seas. Section 6 discusses the implications, and Section 7 concludes.