This study presents an experimental investigation into the use of individual blade pitching as a control strategy to enhance the performance of vertical-axis wind turbines (VAWTs). The research demonstrates that optimal blade pitching kinematics can significantly improve the power coefficient and reduce structural load fluctuations in VAWTs, particularly under off-design operating conditions. A scaled-down turbine model was used in conjunction with a genetic algorithm optimiser to identify optimal pitching profiles for both on- and off-design tip-speed ratios. The results show a threefold increase in power coefficient and a 77% reduction in load fluctuations compared to non-actuated turbines. The study reveals that blade pitching manipulates flow structures to enhance performance by delaying flow separation and controlling vortex shedding. The optimal pitching kinematics involve an outward pitch during the upwind phase and an inward pitch during the downwind phase, which helps to reduce the effective angle of attack and promote flow reattachment. These findings suggest that individual blade pitching is a promising control strategy for improving the efficiency and structural integrity of VAWTs. The study also highlights the importance of considering the aerodynamic complexity of VAWTs in their industrial development and deployment. The results have implications for the future of wind energy, as VAWTs can complement horizontal-axis turbines and help meet global energy demands. The study underscores the need for further research into the effects of optimal blade pitching on aerodynamic performance and the potential for scaling up the findings to real-world applications.This study presents an experimental investigation into the use of individual blade pitching as a control strategy to enhance the performance of vertical-axis wind turbines (VAWTs). The research demonstrates that optimal blade pitching kinematics can significantly improve the power coefficient and reduce structural load fluctuations in VAWTs, particularly under off-design operating conditions. A scaled-down turbine model was used in conjunction with a genetic algorithm optimiser to identify optimal pitching profiles for both on- and off-design tip-speed ratios. The results show a threefold increase in power coefficient and a 77% reduction in load fluctuations compared to non-actuated turbines. The study reveals that blade pitching manipulates flow structures to enhance performance by delaying flow separation and controlling vortex shedding. The optimal pitching kinematics involve an outward pitch during the upwind phase and an inward pitch during the downwind phase, which helps to reduce the effective angle of attack and promote flow reattachment. These findings suggest that individual blade pitching is a promising control strategy for improving the efficiency and structural integrity of VAWTs. The study also highlights the importance of considering the aerodynamic complexity of VAWTs in their industrial development and deployment. The results have implications for the future of wind energy, as VAWTs can complement horizontal-axis turbines and help meet global energy demands. The study underscores the need for further research into the effects of optimal blade pitching on aerodynamic performance and the potential for scaling up the findings to real-world applications.