Modern wind turbines require careful tuning of controller and estimator parameters. However, tuning requires expert control experience, and is therefore in practice often performed by a trial-and-error brute-force approach. The contribution of this work is twofold. Firstly, a framework for tuning the parameters for conventional control and estimator architectures with Bayesian optimization is proposed. Secondly, the proposed scheme is applied to the problem of tuning Kalman filter parameters for the estimation of the rotor effective wind speed. For accomplishing the beforementioned task, the Bayesian optimization machine learning algorithm uses entropy search as utility function. The NREL 5-MW reference wind turbine is used in high-fidelity simulation software to show the efficacy of the proposed methodology. The Bayesian optimized Kalman filter configuration, is shown to estimate the rotor effective wind speed with a root mean square error smaller than 5 %, with respect to the actual effective wind speed over all load cases.

This paper establishes an adaptive tracking approach for linear systems with parametric uncertainties, when input measurements are quantized due to the presence of a communication network closing the control loop. In order to address the tracking problem, a novel dynamic quantizer with dynamic offset is introduced and embedded into an adaptive hybrid control strategy based on zooming mechanism. A Lyapunov-based approach is used to derive the adaptive adjustments for the control gains and for the dynamic range and dynamic offset of the quantizer: it is proven analytically that the proposed adjustments guarantee asymptotic state tracking. Quantized adaptive control of an electrohydraulic system is given as an example of the effectiveness of the designed control methodology.

This paper addresses the problem of asymptotic tracking for switched linear systems with parametric uncertainties and dwell-time switching, when input measurements are quantized due to the presence of a communication network closing the control loop. The problem is solved via a dynamic quantizer with dynamic offset that, embedded in a model reference adaptive control framework, allows the design of the adaptive adjustments for the control parameters and for the dynamic range and dynamic offset of the quantizer. The overall design is carried out via a Lyapunov-based zooming procedure, whose main feature is overcoming the need for zooming out at every switching instant, in order to compensate for the possible increment of the Lyapunov function at the switching instants. It is proven analytically that the resulting adjustments guarantee asymptotic state tracking. The proposed quantized adaptive control is applied to the piecewise linear model of the NASA Generic Transport Model aircraft linearized at multiple operating points.