arXiv subjects

John V. Ringwood

Publications and source records attributed to John V. Ringwood.

5 recordsLinked to original sources

arXiv2026

Control of hybrid wind-wave energy systems using reinforcement learning

Integrating wave energy converters (WECs) with floating offshore wind turbines (FOWTs), to form hybrid wind-wave energy (HWWE) systems, is a promising approach to achieve further cost reduction for offshore renewable energy. In such systems, the control of the integrated WECs plays an important role, with the potential to generate additional wave energy while simultaneously suppressing floating platform motion. However, HWWE systems are characterized by complex dynamics, making accurate modelling only viable through numerical simulation, and posing significant challenges for control design. This paper proposes a reinforcement learning (RL) control framework for HWWE systems, in which the real-time control policy is learned directly through interactions with high-fidelity simulation. A numerical model is established for a HWWE system consisting of an IEA 15 MW wind turbine, a VolturnUS semi-submersible platform, and three torus-type WECs, which is then employed as the RL training environment. Control performance is evaluated in terms of both wave energy generation and platform motion reduction, two competing objectives, from a Pareto perspective. It is shown that the proposed RL controller achieves substantial Pareto improvements over conventional control strategies, e.g., over 75\% higher wave energy capture at the same platform motion level, or nearly 50\% lower motion at the same energy capture level, thereby significantly extending the attainable performance boundary of HWWE systems.

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arXiv2026

Symphony: Simple Phase Control for Wave Energy Systems

Despite significant advancements in controlling wave energy converters, efficiently achieving real-time energy-maximising control remains a challenge. This paper addresses this obstacle by introducing Symphony, a novel non-optimisation-based (NOB) controller inspired by phase control techniques for wave energy devices. Symphony is a suboptimal control that inherits all the advantages of NOB controllers, such as simplicity of design, implementation, tuning, and use, while additionally providing hard-motion-constrained reference trajectories and close-to-optimal performance across the evaluated sea states, even in the presence of measurement noise and model uncertainty. Symphony is designed using a hierarchical structure: By relying on an implicit Gaussian differential equation that requires an excitation force estimate, an upper loop provides a close-to-optimal motion-constrained velocity trajectory and a feedforward control term. Then, a user-flexible lower loop tracks the desired velocity trajectory by combining feedback and feedforward terms. In this paper, Symphony performance is assessed in terms of power absorption. Statistically representative results are obtained using multiple sea-state realisations across varying peak wave periods, considering measurement noise and radiation model uncertainty. The results presented in this paper demonstrate the potential of Symphony, which achieves, in most of the evaluated scenarios, more than 90\% of the power absorption obtained by a spectral (optimisation-based) control solution.

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arXiv2026

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly extends mission range and endurance. Reinforcement learning yields capable model-free controllers for station-keeping and trajectory tracking, but optimizing task accuracy alone drives the policy toward oscillatory, energy-wasting actuation. The established remedy subtracts an energy penalty from the reward, yet this sets the task-power trade-off through a single weight with no physical units: a target power level cannot be specified, the weight must be re-tuned for every vehicle and task, and a mismatched weight can even raise power. This paper instead formulates energy-efficient underwater control as a constrained Markov decision process in which average thruster power is subject to an explicit budget, solved with a PPO-Lagrangian algorithm. The power level is set by declaring a budget in physical units, and a single dual variable is updated online to meet it for each vehicle and task, without manual weight search. Across three vehicles and four tasks in the MarineGym simulator, the energy-constrained policy draws the least power in all twelve settings, reducing it by 14--65\% (up to 64.9\%) over a task-only baseline and below an energy-reward baseline everywhere, while remaining the smoothest in ten settings and preserving task accuracy except in one deliberately power-limited regime. Imposing energy as an explicit constraint thus offers a tuning-free route to energy-efficient underwater control that needs no per-vehicle, per-task weight search.

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arXiv2021

Optimizing the Level of Challenge in Stroke Rehabilitation using Iterative Learning Control: a Simulation

The level of challenge in stroke rehabilitation has to be carefully chosen to keep the patient engaged and motivated while not frustrating them. This paper presents a simulation where this level of challenge is automatically optimized using iterative learning control. An iterative learning controller provides a simulated stroke patient with a target task that the patient then learns to execute. Based on the error between the target task and the execution, the controller adjusts the difficulty of the target task for the next trial. The patient is simulated by a nonlinear autoregressive network with exogenous inputs to mimic their sensorimotor system and a second-order model to approximate their elbow joint dynamics. The results of the simulations show that the rehabilitation approach proposed in this paper results in more difficult tasks and a smoother difficulty progression as compared to a rehabilitation approach where the difficulty of the target task is updated according to a threshold.

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arXiv2018

Comparison of frequency estimation methods for wave energy control

A number of wave energy controllers tune the power take-off (PTO) system to the frequency of incident waves. Since real ocean waves are non-stationary by nature and not defined by a single frequency component, the PTO can be either tuned at a constant frequency characterized by the local spectrum, or continuously tuned to a representative wave frequency. In either case, a time-frequency representation of the waves is expected since the wave profile changes over time. This paper discusses about the PTO tuning problem for passive and reactive controllers, in real waves, by comparing different methods for time-varying frequency estimation: the extended Kalman filter (EKF), frequency-locked loop (FLL), and Hilbert-Huang transform (HHT). The aim is to verify the impact of such methods on the absorbed and reactive powers, and the PTO rating. It is shown that the mean estimated frequency of the EKF, and FLL, converges respectively to the mean centroid frequency, and energy frequency, of the excitation force spectrum. Moreover, the HHT mean frequency has no correlation with the spectral statistical properties. A comparison of the energy absorbed shows that up to 37% more energy is obtained with the HHT over the other estimation methods. Numerical simulations are performed with sea elevation data from the Irish coast.

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