Development and Testing of an Unmanned Aerial Vehicle for Large Scale Particle Image Velocimetry

Large scale particle image velocimetry (LSPIV) is a nonintrusive environmental monitoring methodology that allows for continuous characterization of surface flows in natural catchments. Despite its promise, the implementation of LSPIV in natural environments is limited to areas accessible to human operators. In this work, we propose a novel experimental configuration that allows for unsupervised LSPIV over large water bodies. Specifically, we design, develop, and characterize a lightweight, low cost, and stable quadricopter hosting a digital acquisition system. An active gimbal maintains the camera lens orthogonal to the water surface, thus preventing severe image distortions. Field experiments are performed to characterize the vehicle and assess the feasibility of the approach. We demonstrate that the quadricopter can hover above an area of 1×1m2 for 4–5 minutes with a payload of 500g. Further, LSPIV measurements on a natural stream confirm that the methodology can be reliably used for surface flow studies.

Building Stiffness Estimation by Wave Traveling Times

A novel technique to estimate stiffness in buildings is presented. In contrast with most of the available work in the literature that resorts to diverse forms of modal analysis, this local technique is based on the propagation of a Ricker pulse through the structure and on measuring the wave arrival times at each story of the building, represented as a single layer in a multiple stratum model. These arrival times are later used to recuperate building stiffness at each story. Wave propagation is based on the Thomson-Haskell method, that allows to generalize the wave propagation method to multi-story buildings without significant changes to the original formulation. The number of calculated parameters is small in comparison with methods based on modal analysis. This technique provides and quick and easy methodology to assess building integrity and is an interesting alternative to verify results obtained by other identification methods. Simulation results for building with heterogeneous characteristics across the stories confirm the feasibility of the proposal.

Non-Linear Control of Tilting-Quadcopter Using Feedback Linearization Based Motion Control

In this paper, a nonlinear control of a tilting rotor quadcopter is presented. The overall control architecture is divided into two sub-controllers. The first controller is based on the feedback linearization control derived from the dynamic model of the tilting quadcopter. This controls the pitch, roll, and yaw motions required for movement along an arbitrary trajectory in space. The second controller is based on two PD controllers which are used to control the tilting of the quadcopter independently along the pitch and the yaw directions respectively. The overall control enables the quadcopter to combine tilting and movement along a desired trajectory simultaneously. Simulation studies are presented based on the developed nonlinear dynamic model of the tilting rotor quadcopter to demonstrate the validity and effectiveness of the overall control system for an arbitrary trajectory tracking.

Dynamic Model and Simulation of Flag Vibrations Modeled as a Membrane

A flag is modeled as a membrane to investigate the two-dimensional characteristics of the vibration response to an uniform wind flow. Both the affecting tension and pressure functions for the wind flow with constant velocity are introduced and utilized in the modeling. In this case, the tension is caused by the weight of the flag. The pressure function is a function describing the pressure variations caused on the flag when in uniform flow. The pressure function is found by assuming that the air flow is relatively slow and that the flag is wide enough to minimize cross flow at the boundaries. An analysis of the downstream motion of the flag is necessary as well. Hamilton’s principle is employed to derive the partial differential equation of motion. The flag is oriented in the vertical direction to neglect the effect of the flag’s weight on the system’s response. Galerkin’s method is used to solve for the first four mode shapes of the system, and the system response is numerically solved. Simulations reveal a very reasonable model when the flag is modeled as a membrane.

Dynamics and Control of Dual-Hoist Cranes Moving Triangular Payloads

Certain heavy-lifting applications require the coordinated movement of multiple cranes. Such tasks dramatically increase the complexity of crane operation, especially when the payload has a non-uniform shape. This paper studies the dynamic behavior of a dual-hoist bridge crane moving triangular payloads. Simulations and experiments are used to develop an understanding of the dynamic response of the system. Various inputs and system configurations are analyzed, and important response characteristics are highlighted.

Adaptive Mismatch Compensation for Rate Integrating Vibratory Gyroscopes With Improved Convergence Rate

This paper presents an online adaptive algorithm to compensate damping and stiffness frequency mismatches in rate integrating Coriolis Vibratory Gyroscopes (CVGs). The proposed adaptive compensator consists of a least square estimator that estimates the damping and frequency mismatches, and an online compensator that corrects the mismatches. In order to improve the adaptive compensator’s convergence rate, we introduce a calibration phase where we identify relations between the unknown parameters (i.e. mismatches, rotation rate and rotation angle). Calibration results show that the unknown parameters lie on a hyperplane. When the gyro is in operation, we project parameters estimated from the least square estimator onto the hyperplane. The projection will reduce the degrees of freedom in parameter estimates, thus guaranteeing persistence of excitation and improving convergence rate. Simulation results show that utilization of the projection method will drastically improve convergence rate of the least square estimator and improve gyro performance.

Modeling and Analysis of a CNG Residential Refueling System

Natural gas has recently been proposed as an alternative fuel for transportation in the United States. Refueling infrastructure is the major technological barrier to the market penetration of passenger compressed natural gas (CNG) vehicles. Currently, there is about one natural gas refueling station every 150 gasoline pumps. Nevertheless, natural gas is widely available in American houses, and thus distributed residential refueling is seen as a viable solution. Generally, residential CNG refueling systems use compressors driven by electric motors. With a potential increase in the number of residential natural gas refueling systems over the next few years, the additional load that this system will introduce on the electric power infrastructure can be significant. In this paper, a system dynamic model of a residential refueling system has been developed and validated against data available in the literature. Ultimately, the model will allow for exploring the impact of residential refueling of CNG vehicles on the electric power infrastructure.

On Derivative Sampling From Image Blur for Reconstruction of Band-Limited Signals

Image sensors are typically characterized by slow sampling rates, which limit their efficacy in signal reconstruction applications. Their integrative nature though produces image blur when the exposure window is long enough to capture relative motion of the observed object relative to the sensor. Image blur contains more information on the observed dynamics than the typically used centroids, i.e., time averages of the motion within the exposure window. Parameters characterizing the observed motion, such as the signal derivatives at specified sampling instants, can be used for signal reconstruction through the derivative sampling extension of the known sampling theorem. Using slow image based sensors as derivative samplers allows for reconstruction of faster signals, overcoming Nyquist limitations. In this manuscript, we present an algorithm to extract values of a signal and its derivatives from blurred image measurements at specified sampling instants, i.e. the center of the exposure windows, show its application in two signal reconstruction numerical examples and provide a numerical study on the sensitivity of the extracted values to significant problem parameters.

Variable Fidelity Modeling in Closed Loop Dynamical Systems

In the early stages of control system development, designers often require multiple iterations for purposes of validating control designs in simulation. This has the potential to make high fidelity models undesirable due to increased computational complexity and time required for simulation. As a solution, lower fidelity or simplified models are used for initial designs before controllers are tested on higher fidelity models. In the event that unmodeled dynamics cause the controller to fail when applied on a higher fidelity model, an iterative approach involving designing and validating a controller’s performance may be required. In this paper, a switched-fidelity modeling formulation for closed loop dynamical systems is proposed to reduce computational effort while maintaining elevated accuracy levels of system outputs and control inputs. The effects on computational effort and accuracy are investigated by applying the formulation to a traditional vapor compression system with high and low fidelity models of the evaporator and condenser. This sample case showed the ability of the switched fidelity framework to closely match the outputs and inputs of the high fidelity model while decreasing computational cost by 32% from the high fidelity model. For contrast, the low fidelity model decreases computational cost by 48% relative to the high fidelity model.

Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.