Belinda A. Batten

The Detection of Unsteady Flow Separation with Bioinspired Hair-Cell Sensors

Biologists hypothesize that thousands of micro-scale hairs found on bat wings function as a network of air-flow sensors as part of a biological feedback flow control loop. In this work, we investigate hair-cell sensors as a means of detecting flow features in an unsteady separating flow over a cylinder. Individual hair-cell sensors were modeled using an EulerBernoulli beam equation forced by the fluid flow. When multiple sensor simulations are combined into an array of hair-cells, the response is shown to detect the onset and span of flow reversal, the upstream movement of the point of zero wall shear-stress, and the formation and growth of eddies near the wall of a cylinder. A linear algebraic hair-cell model, written as a function of the flow velocity, is also derived and shown to capture the same features as the hair-cell array simulation.

Reduced-order compensators via balancing and central control design for a structural control problem

Real-time control of a physical system necessitates controllers that are low order. In this article, we compare two balanced truncation methods as a means of designing low-order controllers for a nonlinear cable-mass system. The first is the standard technique of balanced truncation. The second, linear quadratic Gaussian (LQG) balanced truncation, can be thought of as balancing based on the controller, and states that are important from the perspective of control and filter design are retained. The control design applied to each reduced-order model is the central controller. We provide an overview of the central controller and devote attention to the design of this controller in the presence of balancing. Also described in this article is a method for reducing computational time in solving algebraic Riccati equations for the design of low-order LQG balanced controllers.

Active control of a vertical axis pendulum wave energy converter

Presented is a preliminary investigation into the modeling, active control design, and simulation of a vertical axis pendulum wave energy converter, or vertical axis PWEC. Seeking to leverage their promising potential for energy extraction, an active control strategy for the PWEC pendulum dynamics is developed such that net electric power production is increased. Equations of motion for a generic PWEC power take off system are developed. Finally, an active control strategy is derived, being based on optimal and model predictive control theories. Simulations of the generic vertical axis PWEC, both with and without active control, are implemented within a representative Oregon irregular ocean wave environment. Comparisons of the simulations indicate strong increases in net PWEC electric power generation by actively controlling the pendulums dynamics. Future pathways for active control development and PWEC advancement are then proposed.

Use of artificial neural networks for real-time prediction of heave displacement in ocean buoys

Many advanced control systems for wave energy converters (WECs) require knowledge of incoming wave profiles to be implemented. This is due to the non-causal relationship between water elevation and force exerted on a floating body. This study focuses on the use of cascade feedforward neural networks to predict short-term incoming water surface displacements based on recently observed data in real time. Prediction networks are trained with time series data reconstructed from spectral data and recorded time series data from a data buoy deployed off the West Irish Coast. Both training methods are shown to have predictive capabilities with regression coefficients between 0.8-0.9 for a small range of sea states. Both networks prediction accuracies are tested on a large range of sea states as well. For sea states dramatically different from training data prediction accuracies decrease, but less so for the network trained on observed data. The need for accurate wave predictions in the field of WEC control design is also discussed.

Sensor placement for flexible wing shape control

In a series of recent papers, shape control of a flexible plate through piezoceramic actuation has been presented. In that work, full-state feedback was assumed. In this paper, we move to sensor placement for design of an observer. Piezoceramic actuators and sensors for strain and velocity will be used. We will compare the performance of three systems: one with a distribution of sensors based on predictions of high strain regions based on elasticity theory; another with sensors placed manually based on bending gain features; and a third with sensors placed using the centroidal Voronoi tesselation with functional gains as placement weights. We compare the effectiveness of the systems in achieving a desired tip position in the presence of disturbances.

Balanced Truncation Model Reduction of a Nonlinear Cable-Mass PDE System with Interior Damping

We consider model order reduction of a nonlinear cable-mass system modeled by a 1D wave equation with interior damping and dynamic boundary conditions. The system is driven by a time dependent forcing input to a linear mass-spring system at one boundary. The goal of the model reduction is to produce a low order model that produces an accurate approximation to the displacement and velocity of the mass in the nonlinear mass-spring system at the opposite boundary. We first prove that the linearized and nonlinear unforced systems are well-posed and exponentially stable under certain conditions on the damping parameters, and then consider a balanced truncation method to generate the reduced order model (ROM) of the nonlinear input-output system. Little is known about model reduction of nonlinear input-output systems, and so we present detailed numerical experiments concerning the performance of the nonlinear ROM. We find that the ROM is accurate for many different combinations of model parameters.