Hierarchical Robust Control of Oscillating Wave Energy Converters With Uncertain Dynamics

Fusco, Francesco; Ringwood, John V.

doi:10.1109/tste.2014.2313479

Cited by 78 publications

(47 citation statements)

References 20 publications

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“…Therefore, it is important to use the filter (F), which is usually a low-pass filter, to reduce the influence of high-frequency modeling errors. In addition, the filter smoothes out noisy or rapidly changing signals and reduces the transient response of the control signal [24]. As can be concluded from Fig.…”

Section: Internal Model Control (Imc)mentioning

confidence: 90%

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Wavelet Neural Network-Based NARMA-L2 Internal Model Control Utilizing Micro-artificial Immune Techniques to Control Nonlinear Systems

Lutfy

Selamat

2015

Arab J Sci Eng

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This paper presents an intelligent control strategy based on internal model control (IMC) to control nonlinear systems. In particular, a wavelet neural network (WNN)-based nonlinear autoregressive moving average (NARMA-L2) network is used to acquire the forward dynamics of the controlled system. Subsequently, the control law can be directly derived. In this approach, a single NARMA-L2 with only one training phase is required. Hence, unlike other related works, this design approach does not require an additional training phase to find the model inversion. In the literature, gradient descent methods are the most widely applied training techniques for the neural network-based IMC. However, these methods are characterized by the slow convergence speed and the tendency to get trapped at local minima. To avoid these limitations, the newly developed modified micro-artificial immune system (modified Micro-AIS) is employed in this work to train the NARMA-L2. The simulation results have demonstrated the effectiveness of the proposed approach in terms of accurate control and robustness against external disturbances. In addition, a comparative study has shown the superiority of the WNN over the multilayer perceptron and the radial basis function based IMC. Moreover, compared with the genetic algorithm, the modified Micro-AIS has achieved better results as the training method in the IMC structure.

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Section: Internal Model Control (Imc)mentioning

confidence: 90%

“…4 introduces robustness into the IMC structure to counteract the modeling errors. In addition, it smoothes out the rapidly changing signals in order to reduce the transient response of the IMC controller [9,24]. The first-order robustness filter equation is given by:…”

Section: The Overall Wnn-based Narma-l2 Internal Model Control Structurementioning

confidence: 99%

Wavelet Neural Network-Based NARMA-L2 Internal Model Control Utilizing Micro-artificial Immune Techniques to Control Nonlinear Systems

Lutfy

Selamat

2015

Arab J Sci Eng

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“…The similar idea can be found in Ref. [15] where the classical linear proportion of the wave excitation force and the internal mode control are utilized as higher and lower control levels, respectively. In this study, an off-line optimization problem with objective of maximizing the absorbed energy subject to physical constraints of the buoy motion is deployed in the higher level control.…”

Section: Introductionmentioning

confidence: 89%

Robust and low computational cost controller for improving captured power in heaving wave energy converters

et al. 2015

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a b s t r a c tThis study suggests a simple and robust control strategy for improving captured power in heaving wave energy converters (WEC). The controller aims at optimizing the captured power using a hierarchical control strategy (HCS), which consists of a higher and lower control levels. The top level control is responsible of producing the reference velocity that is used by the lower level controller to regulate the actual velocity of the WEC buoy. The reference is generated by solving an offline optimization problem for maximizing the absorbed energy subject to the buoy motion constraints. As an attempt to avoid computational complexity in real-time operation, a simple look-up table is produced for various seastates conditions. In order to track this reference, sliding mode controller (SMC) is deployed. The design parameters of the SMC is tuned offline using a well-known model of WEC. Any perturbations, nonlinearities, and disturbances not covered by the used model will be handled by the robustness property of the SMC. Moreover, the used SMC does not add any further complexity to the overall control strategy. The proposed method is validated in both regular and irregular sea-states, as well as in both nominal and perturbed scenarios. Other control strategies were also used to further assess the performance of the proposed control strategy. Based on the simulation results, the proposed hierarchical controller outperformed the other controllers in both nominal and perturbed scenarios.

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“…2) Trajectory tracking: A backstepping method is implemented for trajectory tracking, forcing the nonlinear dynamical WEC system to follow the optimal trajectory calculated by the linear control model. Backstepping, employing feedback linearisation, is shown to have good robustness properties [26] and follows the general robust hierarchical structure in [27]. The implementation of the backstepping method is described in [13], where it is shown to minimise the position error, e 2 (t), and the velocity error, e 2 (t):…”

Section: ) Optimal Trajectory Calculationmentioning

confidence: 99%

Adaptive Control of a Wave Energy Converter

Davidson

Genest

Ringwood

2018

IEEE Trans. Sustain. Energy

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Energy maximising controllers (EMCs), for wave energy converters (WECs), based on linear models, are attractive in terms of simplicity and computation. However, such (Cummins equation) models are normally built around the still water level as an equilibrium point and assume small movement, leading to poor model validity for realistic WEC motions, especially for the large amplitude motions obtained by a well controlled WEC. The method proposed here is to use an adaptive algorithm to estimate the control model online, whereby system identification techniques are employed to identify a linear model that is most representative of the actual controlled WEC behaviour. Using exponential forgetting, the linear model can be regularly adapted to remain representative in changing operational conditions. To that end, this paper presents a novel adaptive controller based on a receding horizon pseudospectral formulation. A case study is presented, providing an preliminary evaluation of the adaptive receding horizon pseudospectral control (ARHPC), using simulations based on both linear hydrodynamic modelling and computational fluid dynamics (CFD). The parameter adaptation is shown to behave as expected in both different simulation environments. The ARHPC is observed to perform well in irregular sea states, absorbing more power than its nonadaptive counterpart. The optimal trajectory calculated by the adaptive model is seen to have a smaller motion and power takeoff (PTO) forces, compared to those calculated by the nonadaptive linear control model, due to the increased amount of hydrodynamic resistance estimated by the adaptive model, as identified from the nonlinear viscous CFD simulation.

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Hierarchical Robust Control of Oscillating Wave Energy Converters With Uncertain Dynamics

Cited by 78 publications

References 20 publications

Wavelet Neural Network-Based NARMA-L2 Internal Model Control Utilizing Micro-artificial Immune Techniques to Control Nonlinear Systems

Wavelet Neural Network-Based NARMA-L2 Internal Model Control Utilizing Micro-artificial Immune Techniques to Control Nonlinear Systems

Robust and low computational cost controller for improving captured power in heaving wave energy converters

Adaptive Control of a Wave Energy Converter

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