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Wind turbine components are subject to considerable stress and fatigue due to extreme environmental conditions to which they are exposed to, especially when located offshore. Interest in the integration of control with system health monitoring has increased in recent years. The integration of a health management module with model predictive control (MPC) provides the wind turbine a mechanism to operate safely and optimize the trade-off between components’ life and energy production. In this paper, a health-aware LPV model predictive control approach for wind turbines is proposed. The proposed controller establishes a trade-off between the economic objective based on maximizing the energy production but at the same time taking into account the minimization of accumulated stress on the wind turbine blades. The controller uses an LPV model for dealing with the non-linearity of the wind turbine model and the inclusion of the stress model. The proposed approach is tested on a well-known wind turbine case.

A robust economic Model Predictive Control (EMPC) approach is presented in this paper for the control of a Drinking Water Network (DWN) albeit the presence of uncertainties in the forecasted demands required for the predictive control design. The uncertain forecasted demand on the nominal MPC has the possibility of rendering the optimization process infeasible or degrade the controller performance. In this paper, the uncertainty on demand is considered unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC is formulated to ensure robust constraint satisfaction, performance and stability of the MPC for DWN to meet user requirements whilst ensuring lower operational cost for water utility operators.