This research contributes to a novel non-contact installation method for offshore wind turbines, utilizing electromagnetism for positional control. Floating wind installation requires efficient control methods for payload positioning. Current methods use tuggerlines that are physically attached and work through tensile attractive forces. In contrast, electromagnetic control offers a non-contact solution and allows for control through both attractive and repulsive forces. This research explores the applicability of multiple data-driven control algorithms on a magnetically controlled pendulum system. These algorithms are evaluated for both positional control and motion attenuation under pivot-point excitation, using simulations of a numerical model of the electromagnetic pendulum. The model-free control algorithms considered in this study assumed that changes in the system’s state are solely due to the control output. These methods aimed to optimize the control output based on either the state error or estimates of the state gradient with respect to the control output. However, this assumption was found to be valid only for systems dominated by the control response with limited dynamic behaviour over time, which was not the case for the electromagnetic pendulum system. The study culminates in the development of a controller based on the online estimation of the magnetic interaction force. This controller, named Proportional-Derivative Force estimating neural network controller (PD-FeNN), is a neural-network based state-dependent PD-controller. The neural network is trained during an operational learning phase on estimations of the magnetic interaction force, which are derived using the model of a linear undamped pendulum. By incorporating the neural network, this approach eliminates the requirement of the previous state-of-the-art controller to manual model the magnetic interaction force based on experimental data. The PD-FeNN controller is tested on both numerical and physical models of the electromagnetically controlled pendulum. The results demonstrate efficient control across a wide range of excitation frequencies and amplitudes for both motion attenuation and positional control. As a successor to the modified PD controller, the PD-FeNN controller improves upon its predecessor by enabling positional control without requiring a model of the magnetic interaction. This advancement enhances the applicability of non-contact motion control for payloads in the offshore wind industry.

This research contributes to a novel non-contact installation method for offshore wind turbines, utilizing electromagnetism for positional control. Floating wind installation requires efficient control methods for payload positioning. Current methods use tuggerlines that are physically attached and work through tensile attractive forces. In contrast, electromagnetic control offers a non-contact solution and allows for control through both attractive and repulsive forces. This research explores the applicability of multiple data-driven control algorithms on a magnetically controlled pendulum system. These algorithms are evaluated for both positional control and motion attenuation under pivot-point excitation, using simulations of a numerical model of the electromagnetic pendulum. The model-free control algorithms considered in this study assumed that changes in the system’s state are solely due to the control output. These methods aimed to optimize the control output based on either the state error or estimates of the state gradient with respect to the control output. However, this assumption was found to be valid only for systems dominated by the control response with limited dynamic behaviour over time, which was not the case for the electromagnetic pendulum system. The study culminates in the development of a controller based on the online estimation of the magnetic interaction force. This controller, named Proportional-Derivative Force estimating neural network controller (PD-FeNN), is a neural-network based state-dependent PD-controller. The neural network is trained during an operational learning phase on estimations of the magnetic interaction force, which are derived using the model of a linear undamped pendulum. By incorporating the neural network, this approach eliminates the requirement of the previous state-of-the-art controller to manual model the magnetic interaction force based on experimental data. The PD-FeNN controller is tested on both numerical and physical models of the electromagnetically controlled pendulum. The results demonstrate efficient control across a wide range of excitation frequencies and amplitudes for both motion attenuation and positional control. As a successor to the modified PD controller, the PD-FeNN controller improves upon its predecessor by enabling positional control without requiring a model of the magnetic interaction. This advancement enhances the applicability of non-contact motion control for payloads in the offshore wind industry.