ν-gap metric based multi-model predictive control of an ocean current turbine system with blade pitch failures

In this article, a method of combining multiple linear models is developed and used by an advanced model predictive control algorithm for ocean current turbine flight control. The developed model bank consists of faulty and healthy linear models created for the ocean current turbine system. Each linear model is used to compute individual control actions using the model predictive control framework, which are combined to generate the aggregate control action that is applied to the system. A weighted average defined by the nu-gap distance between the current linear approximation of system dynamics and each individual linear system is used to compute the aggregate control action. The multi-model predictive control strategy is applied to control the ocean current turbine to mitigate performance degradation that may occur due to failures in the system. The proposed control framework's effectiveness is shown with the renewable power generation improvement during a faulty case.