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The integration of hydrokinetic turbine (HKT) systems relies heavily on power converters, which enable precise control of turbine rotational speed to regulate active power delivered to the grid while providing reactive power compensation. This paper introduces an advanced model predictive control (MPC) strategy designed for maximum power point tracking and output frequency regulation in an HKT system interfaced through a back-to-back converter. The setup comprises a horizontal propeller turbine, a permanent magnet synchronous generator, a rectifier, and an inverter. The proposed control scheme was implemented in MATLAB and validated via simulations, with performance assessed through the system’s state variables. A comparison with a conventional proportional-integral (PI) control method demonstrates that MPC delivers superior transient performance and shorter settling times for high-efficiency operation, while remaining straightforward to implement. Specifically, the MPC controller exhibited a settling time that was 4 times faster than that of the PI controller. 

This paper introduces a benchmark comparison of distributed optimization algorithms based on dual decomposition for model predictive control problems with constraint coupling. Such problems can be viewed as multiple subsystems linked through constraints on shared, limited resources. In a dual decomposition approach, the allocation and use of these resources are coordinated by repeatedly calculating their associated prices and distributing those values to the participating subsystems. After a short overview of model predictive control, various architectures and communication structures suitable for distributed implementations are outlined. The centralized control problem is then decomposed into several subproblems by introducing dual variables. A range of distributed optimization methods is examined, including the recently developed quasi-Newton dual ascent algorithm. Additionally, an epigraph reformulation of the bundle, combined with a line search technique, is proposed for the quasi-Newton dual ascent method; these enhancements improve its numerical stability and accelerate convergence relative to the earlier trust-region approach. Lastly, the quasi-Newton dual ascent algorithm is benchmarked against the subgradient method, the bundle trust method, and the alternating direction method of multipliers across a broad set of test problems. All benchmark instances are openly accessible on GitHub.