Nonlinear Constrained Optimal Control of Wave Energy Converters With Adaptive Dynamic Programming

In this paper, we address the energy maximization problem of wave energy converters (WEC) subject to nonlinearities and constraints, and present an efficient online control strategy based on the principle of adaptive dynamic programming (ADP) for solving the associated Hamilton-Jacobi-Bellman equati...

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Published inIEEE transactions on industrial electronics (1982) Vol. 66; no. 10; pp. 7904 - 7915
Main Authors Na, Jing, Wang, Bin, Li, Guang, Zhan, Siyuan, He, Wei
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptation models
Adaptive control
Adaptive dynamic programming (ADP)
Computational modeling
Computer simulation
constrained optimal control
Constraint modelling
Converters
Dynamic programming
Force
Hydraulic systems
Neural networks
Nonlinear control
Open area test sites
Optimal control
Optimization
wave energy converters (WEC)
Wave power
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Abstract In this paper, we address the energy maximization problem of wave energy converters (WEC) subject to nonlinearities and constraints, and present an efficient online control strategy based on the principle of adaptive dynamic programming (ADP) for solving the associated Hamilton-Jacobi-Bellman equation. To solve the derived constrained nonlinear optimal control problem, a critic neural network (NN) is used to approximate the time-dependant optimal cost value and then calculate the practical suboptimal causal control action. The proposed novel WEC control strategy leads to a simplified ADP framework without involving the widely used actor NN. The significantly improved computational efficacy of the proposed control makes it attractive for its practical implementation on a WEC to achieve a reduced unit cost of energy output, which is especially important when the dynamics of a WEC are complicated and need to be described accurately by a high-order model with nonlinearities and constraints. Simulation results are provided to show the efficacy of the proposed control method.
AbstractList In this paper, we address the energy maximization problem of wave energy converters (WEC) subject to nonlinearities and constraints, and present an efficient online control strategy based on the principle of adaptive dynamic programming (ADP) for solving the associated Hamilton-Jacobi-Bellman equation. To solve the derived constrained nonlinear optimal control problem, a critic neural network (NN) is used to approximate the time-dependant optimal cost value and then calculate the practical suboptimal causal control action. The proposed novel WEC control strategy leads to a simplified ADP framework without involving the widely used actor NN. The significantly improved computational efficacy of the proposed control makes it attractive for its practical implementation on a WEC to achieve a reduced unit cost of energy output, which is especially important when the dynamics of a WEC are complicated and need to be described accurately by a high-order model with nonlinearities and constraints. Simulation results are provided to show the efficacy of the proposed control method.
Author Zhan, Siyuan
Li, Guang
Na, Jing
He, Wei
Wang, Bin
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Snippet In this paper, we address the energy maximization problem of wave energy converters (WEC) subject to nonlinearities and constraints, and present an efficient...
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SubjectTerms Adaptation models
Adaptive control
Adaptive dynamic programming (ADP)
Computational modeling
Computer simulation
constrained optimal control
Constraint modelling
Converters
Dynamic programming
Force
Hydraulic systems
Neural networks
Nonlinear control
Open area test sites
Optimal control
Optimization
wave energy converters (WEC)
Wave power
Title Nonlinear Constrained Optimal Control of Wave Energy Converters With Adaptive Dynamic Programming
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