Floating offshore wind (FOW) is an emerging technology capable of harnessing wind energy in waters too deep for bottom-fixed wind (BFW) turbines. However, this presents additional design, construction, installation and maintenance challenges, leading to higher costs of FOW than BFW. Therefore, strategies capable of reducing these costs are highly desirable. One such strategy is LIDAR-assisted pitch control (LAC), which utilises nacelle-mounted LIDAR to measure the velocity of the incoming wind, allowing the turbine to use feedforward (FF) control to actuate its blade pitch systems in advance of the wind’s impact. Individual blade pitch control (IPC) can also be employed to mitigate variations in structural loads. This thesis presents a novel LIDAR-assisted FF IPC (FFIPC) approach, which was combined with an FF-feedback collective pitch controller (FFCPC). The source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LAC. Simulations of a 15 MW wind turbine mounted on two different floating platforms, a semi-submersible and a spar, were performed. For both FOW turbines (FOWTs), the FF controllers delivered standard deviation reductions of up to 75% in the rotor speed and power, 58% in the platform pitch and surge and 20% in the tower base fore-aft bending moment compared to the baseline feedback (FB)-only controller. The semi-submersible benefitted more from the combination of FFCPC and FFIPC (FFCPC+FFIPC), with up to 12% further reductions to the standard deviations compared to the FFCPC on its own. The spar did not benefit as significantly from the addition of FFIPC, with FB IPC (FBIPC) instead better complimenting the FFCPC. The implementation of FFCPC+FFIPC within the 15 MW semi-submersible was applied to three case studies aiming to quantify the cost benefits of LAC for FOW. Firstly, the loading reductions were converted to reductions of lifetime component failure rates before being applied within an operations and maintenance (O&M) model. The decreased failure rates brought by LAC delivered reductions to the operational expenditure of FOW farms, by up to 11%. The second study applied modifications to the FOWT’s tower. The results indicated the ability to reduce the tower thickness by up to 20% when using LAC while achieving similar levels of stress to the baseline tower design using FB-only control. Finally, simulations were extended to the array level using FAST.Farm. The addition of LAC was found to deliver improved wake recovery of the FOWTs. This allowed for reduced mean power deficits of downstream turbines at low above-rated wind speeds, which translated to an increase in their annual energy production by up to 0.8%, relative to when they used FB-only control. In summary, this thesis provides strong evidence for the large-scale adoption of LAC within FOWTs, presenting multiple avenues for reducing the levelised cost of FOW energy, which can ultimately increase the commercial viability of FOW projects.