In order to build a carbon-free society, the production of clean and affordable electric energy is vital. Furthermore, to reduce variability and minimise the need for potentially expensive energy storage systems, the diversification of renewable energy resources is essential. To this end, wave energy is a significant and almost untapped source of renewable energy, which can considerably contribute to the renewable energy mix and, ultimately, to decarbonisation. Wave energy converters (WECs) harness wave power by exploiting different operating principles. However, due to the relatively high levelised cost of energy (LCoE) associated with wave energy projects, WECs struggle to penetrate in the electric power industry market. A key step to minimise the LCoE, and therefore improve WEC commercialization, is to develop high-performance, real-time, control strategies to maximise the electric energy produced over the WEC lifetime. In particular, this thesis focuses on enhancing the economic viability of a specific type of WEC, known as the oscillating water column (OWC) WEC, by improving state-of-the-art OWC control techniques. The OWC system is one of the most promising WECs, especially due to its simplicity of operation, the possibility to easily dissipate excessive power, and the fact that all the moving parts are above the water level, meaning that maintenance operations are potentially less complex and expensive. To date, due to the critical importance of turbine performance, OWC control strategies mainly focus on an oversimplified control objective, namely turbine efficiency maximisation, ignoring hydrodynamic and electric generator performance. In this thesis, possible staticefficiency- based and dynamic control strategies to optimise the (overall) wave-towire (W2W) energy conversion process of OWC WECs are designed. Furthermore, as an alternative to relatively laborious model determination from first principles, the potential of data-based modelling techniques to provide parsimonious, controloriented, OWC hydrodynamic models, is investigated. Finally, to maximise OWC WEC profitability, it is important to consider peak-shaving (or rated power) control, to extend the OWC operational range and, consequently, improve the capacity factor. Since peak-shaving control affects the optimal sizing problem for an OWC PTO, a control co-design approach is devised in this thesis to assess the benefit of rated power control. Ultimately, peak-shaving control, used in combination with control co-design techniques, can significantly reduce the LCoE by improving the capacity factor.