Distributed arrays of artificial hair sensors have bio-like sensing capabilities to obtain spatial and temporal surface flow information which is an important aspect of an effective fly-by-feel system. The spatiotemporal surface flow measurement enables further exploration of additional flow features such as flow stagnation, separation, and reattachment points. Due to their inherent robustness and fault tolerant capability, distributed arrays of hair sensors are well equipped to assess the aerodynamic and flow states in adverse conditions. In this paper, a local flow measurement from an array of artificial hair sensors in a wind tunnel experiment is used with a feedforward artificial neural network to predict aerodynamic parameters such as lift coefficient, moment coefficient, free-stream velocity, and angle of attack on an airfoil. We find the prediction error within 6% and 10% for lift and moment coefficients. The error for free-stream velocity and angle of attack were within 0.12 mph and 0.37 degrees. Knowledge of these parameters are key to finding the real time forces and moments which paves the way for effective control design to increase flight agility, stability, and maneuverability.

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This study presents a method to develop an aeroelastic model of a smart section blade equipped with microtab. The model is suitable for potential passive vibration control study of the blade section in classic flutter. Equations of the model are described by the nondimensional flapwise and torsional vibration modes coupled with the aerodynamic model based on the Theodorsen theory and aerodynamic effects of the microtab based on the wind tunnel experimental data. The aeroelastic model is validated using numerical data available in the literature and then utilized to analyze the microtab control capability on flutter instability case and divergence instability case. The effectiveness of the microtab is investigated with the scenarios of different output controllers and actuation deployments for both instability cases. The numerical results show that the microtab can effectively suppress both vibration modes with the appropriate choice of the output feedback controller.

Fly by feel is a concept in which distributed sensors and actuators are integrated on an aerial system for state awareness or sensation of the environment, and make use of distributed control to increase the system maneuverability, stability and safety. Artificial hair sensors are good candidates as sensors for the fly by feel concept because they are lightweight, have low manufacturing costs and can easily be integrated on the surface of air-vehicle without affecting the flow. We investigate an application of artificial hair sensors considering its capability of measuring the local flow velocity combined with a Feedforward Artificial Neural Network to predict the aerodynamic quantities such as lift coefficient, moment coefficient, angle of attack and free-stream velocity in real-time. These quantities, when combined with the physical and unsteady aerodynamics parameters, will make a framework for designing and implementing an active controller for gust alleviation in a pitch and plunge airfoil system.

In this paper, a direct adaptive control approach is used to track the tip speed ratio (TSR) of wind turbine to maximize the power captured during the below rated wind speed operation. Assuming a known optimum value of TSR, the deviation of actual TSR from the optimum one is mathematically expressed as TSR tracking error. Since the actual TSR is not a measurable quantity, this expression for TSR tracking error is linearized and simplified to express it in terms of wind speed and rotor speed, where rotor speed can easily be measured. Although it is possible to measure the wind speed with high accuracy using LiDAR, using it raises the overall cost of wind turbine installation; hence, a method to estimate the wind speed is also proposed. The adaptive controller operates on this simplified TSR tracking error to drive it to zero and to keep the TSR constant at desired optimum value. The performance of the proposed control scheme is illustrated by implementing and simulating it in the National Renewable Energy Laboratory 5MW wind turbine model and comparing the results with the existing baseline fixed gain controller. Copyright © 2015 John Wiley & Sons, Ltd.