Constrained near-optimal control of a wave energy converter in three oscillation modes

Korde, Umesh A.; Lyu, Jianyang; Robinett, Rush D.; Wilson, David G.; Bacelli, Giorgio; Abdelkhalik, Ossama

doi:10.1016/j.apor.2017.10.004

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…The presented results reveal a great improvement in absorbed power over heavy-only WECs. Contrary to these results, in [56] A. Korde states that near-optimal control for pitch-surge motions are not significant for wave energy absorption when compared to heave motion which is presented as the dominant contributor to power absorption.…”

Section: Model Predictive Controlmentioning

confidence: 56%

Control Strategies Applied to Wave Energy Converters: State of the Art

Maria-Arenas¹,

Garrido

Rusu

et al. 2019

Energies

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Wave energy’s path towards commercialization requires maximizing reliability, survivability, an improvement in energy harvested from the wave and efficiency of the wave to wire conversion. In this sense, control strategies directly impact the survivability and safe operation of the device, as well as the ability to harness the energy from the wave. For example, tuning the device’s natural frequency to the incoming wave allows resonance mode operation and amplifies the velocity, which has a quadratic proportionality to the extracted energy. In this article, a review of the main control strategies applied in wave energy conversion is presented along their corresponding power take-off (PTO) systems.

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Section: Model Predictive Controlmentioning

confidence: 56%

Control Strategies Applied to Wave Energy Converters: State of the Art

Maria-Arenas¹,

Garrido

Rusu

et al. 2019

Energies

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“…Several studies [4,883,5,882,416] developed MIMO controllers for WECs assuming full control authority over each hydrodynamic mode. Such studies enable analysis of the significance of individual degrees-of-freedom on overall power generation, but do not take into account any additional geometric coupling that exists in real multimode prototypes.…”

Section: Review Of Advanced Control Strategies Applied To Multi-mode ...mentioning

confidence: 99%

Modelling and Optimisation of Wave Energy Converters

Ning¹,

Ding²

2022

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“…A heave-pitch-surge 3-DOF WEC, with the pitchsurge optimization separated from heave, increased the gathered energy by more than three times compared to the captured energy from the lurch-only WEC (17). Investigation of blocked optimum control on a small asymmetrical float indicated that, despite the improvement in power capture achievable with the current control, the actuation forces are significant (18). Moreover, Burgaç and Yavuz implemented the discrete Fourier transform technique to estimate the dominant wave frequency in the fuzzy controller for defining the power take-off control settings (19).…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning with Immersion- and Invariance-based State Observer Control of Wave Energy Converters

Mohammadian KhalafAnsara,

Keighobadi

2024

IJE

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Composable life under the extensive global warming of the Earth encourages the progress of renewable energy devices and the adoption of new technologies, such as artificial intelligence. Regarding enormous potential of wave energy and its consistency, wave energy converter (WEC) plays vital role in uniform energy harvesting field. In this paper, the significant environmental changes in the ocean prompt us to propose an intelligent feedback control system to mitigate the impact of disturbances and variable wind effects on the efficacy of WECs. Deep reinforcement learning (DRL), as a powerful machine intelligence technique, is capable of identifying WECs as black-box models. Therefore, based on the DRL model, the disturbance and unmeasured state variables are simultaneously estimated in the extended state observer section. Leakage in identification data and real-time application requirements of limited number of layers in the deep neural networks are compensated by implementation of immersion and invariance-based extended state observer which improves coping with the unwanted exogenous noises as well. In the overall intelligent control system, the estimated parameters are inputted into the DRL as the actor-critic networks. The initial actor network is responsible for predicting the control action, while the subsequent critic network determines the decision criterion for evaluating the accuracy of the actor's estimated amount. Next, the output value of the critic stage is backpropagated through the layers to update the network weights. The simulation test results in MATLAB indicate the convergence of unmeasured parameters/states to the corresponding true values and the significance of newly designed intelligent DRL method.

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Constrained near-optimal control of a wave energy converter in three oscillation modes

Cited by 10 publications

References 16 publications

Control Strategies Applied to Wave Energy Converters: State of the Art

Control Strategies Applied to Wave Energy Converters: State of the Art

Modelling and Optimisation of Wave Energy Converters

Deep Reinforcement Learning with Immersion- and Invariance-based State Observer Control of Wave Energy Converters

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