This thesis is aimed at proposing an artificial intelligence controller to maximize the energy absorption of wave energy converter (WEC) in practical application. The controller maximizes the energy absorption by locking and releasing the WEC alternately, and such control is known as the latching control. The model predictive control strategy is used to implement realtime control. Since the control inputs are future wave forces, the controller is a noncausal system. An artificial neural network is developed and trained by the machine learning algorithm to predict the shortterm wave forces.Firstly, the statespace dynamic model is developed to simulate the kinetic motion of the WEC in sea waves. Compared with traditional convolution dynamic model, the statespace representation extremely enhances the computation efficiency. Moreover, the statespace model is represented by a differential formula so that it is more convenient to implement the control algorithm. The statespace model is acquired based on the socalled system identification, which transforms the frequencydomain hydrodynamic coefficients of the WEC into a set of state vectors. A Rankine source boundary element method code is developed to calculate the hydrodynamic coefficients. By separating the entire fluid boundary into a set of elements and distributing the Rankine source across the elements uniformly, the velocity potential of the fluid is determined.The latching control, a kind of phase control, maximizes the energy absorption by tuning the velocity phase and making it in phase with the wave excitation forces. The tuning of velocity phase is fulfilled by locking the WEC at some time instants following the control command. The Pontryagin's maximum principle is applied to derive the control command, which maximizes the energy absorption over a time horizon [ti, ti+p]. By receding the time horizon forward step by step, the control command is updated at each time instant and thereby the control is implemented in a realtime manner. It is the basic idea of the model predictive control. Since the derived control command just maximizes the energy absorption over the time horizon [ti,ti+p] rather than the entire interval [0, T], the model predictive control is a suboptimal control strategy.The inputs to a noncausal wave energy controller are future wave forces, and it is what prevents the practical application of wave energy control. An artificial neural network is developed to predict the shortterm wave forces. Unlike traditional deterministic forecasting approach, which is based on a specific model defined by the users, the neural network forecasts the future wave forces by learning from examples itself since even the users don't know anything about the prediction model. In another word, the neural network is a datadriven prediction approach. The developed neural network is trained by a set of examples using the machine learning algorithm.With the artificial neural network, the realtime smart controller is incorporated into a heavingpoint absorber to maximize the energy absorption. It is shown that the energy absorption is indeed increased substantially even if the wave forces are predicted. It indicates that the realtime controller is applicable in the real practice. Besides, the effects of prediction duration and prediction deviation on the control performance are also investigated. Long prediction duration enhances the control efficiency whereas the energy absorption is reduced with the presence of prediction deviation. Since the deviation accumulates with the prediction duration, a moderate prediction duration is thus recommended.The multistable mechanism of the WEC is also investigated in this thesis. A nonlinear PTO (power takeoff) system with two oblique springs is proposed. The PTO system is either monostable or bistable depending on the initia
Date of Award  17 Mar 2019 

Original language  English 

Awarding Institution   University Of Strathclyde


Sponsors  University of Strathclyde 

Supervisor  Zhiming Yuan (Supervisor) & Sandy Day (Supervisor) 
