In this brief, a novel control system structure for integrated dynamic positioning, maneuvering, and transit operation of marine vessels is proposed. The proposed structure is based on supervisory switching control (SSC) using switching logics in conjunction with operator initiated commands. The SSC is a hybrid system consisting of continuous state multi-controllers and discrete state logics that allow switching among the various controllers for the particular operations. The switching between appropriately designed controllers facilitates operations from normal conditions to extreme situations such as severe seas and possible failure situations. Through this, it will be possible to extend the vessel operability under harsh environments, and increase the safety and performance in marine operations with greater fault-tolerance. One demonstrating example of the integrated marine control system verified by experiments is demonstrated.

In the present industrial thruster-assisted position mooring (PM) systems, thrusters are used to damp the vessel's dynamical motions and to provide compensation of any line breakage. To increase robustness and extend the weather window for PM systems, this paper proposes a new concept of setpoint (SP) chasing for moderate and extreme conditions. The SP in moderate conditions is the vessel's equilibrium position where the environmental loads are balanced by the mooring forces to avoid the conflict between the control action of thruster assistance and the mooring system. To chase this SP, the thruster assistance provides additional damping and restoring forces to compensate the dynamic environmental loads while the mooring system compensates the mean environmental loads. The simulations and experiments suggested that the proposed SP chasing for moderate conditions improved the vessel performance while the utilization of the mooring system was maximized. Due to risk for mooring line breakage, the SP in extreme conditions is the position closer to the field zero point than the equilibrium position. To chase this SP, the thruster assistance provides damping, restoring force and mean force controls to compensate both the dynamic and a part of the mean environmental loads while the mooring system compensates the rest of the mean environmental loads. The simulations indicated that the proposed SP chasing for extreme conditions reduced the vessel's drift and tension in the most loaded line.

The rapid rise of wind energy introduces persistent challenges related to variability, control, and operational reliability. While traditional machine learning supports forecasting and optimization, its scope is narrow and typically operator-dependent. We propose an agentic AI paradigm in which autonomous, goal-directed agents interact with turbines, the wind farm, and the grid to manage complexity in real time. A multi-agent architecture coordinates turbine-, farm-, and grid-level decisions to enhance efficiency and resilience. We present three concise case studies illustrating optimization at complementary scales: Case 1 (turbine-level) an AI agent adjusts upstream derating setpoints to mitigate wake effects and increase overall farm power production, at the cost of reduced power from the derated turbines; Case 2 (farm-level) a coordinating agent aligns multiple turbines to meet plant-wide energy and reliability objectives under operational and environmental constraints; Case 3 (grid-level) a system agent balances wind with other generators and consumer demand while honoring transmission limits and minimizing cost. Together, these cases show how local actions and global coordination increase energy yield, reduce structural loading, and improve reliability. Applications span adaptive forecasting, wake management, predictive maintenance, market participation, and cyber-physical security. We outline research needs in data quality, interoperability, safety, and regulation, and advocate hybrid designs that fuse reinforcement learning with digital twins to advance intelligent wind infrastructures.