In this study a general approach is introduced for the design of a robust control law for suppression of structure borne vibration. This control law is based on a passive design in the form of dynamic vibration absorbers. Passive absorbers minimize vibration at a speci c frequency, but their performance is improved by introducing adaptive tuning of the absorber. An adaptive dynamic vibration absorber is tuned to the forcing frequency, using classical methods. The tuning ratio is time varying and adapts itself to variations in the forcing frequency. However, the uniqueness of the approach in this study is that the damping parameter of the absorber is continuously varied by means of a fuzzy-logic control algorithm to provide a lower sound pressure level. The inputs of the fuzzy control law are the displacement and velocity of the main structure. The effectiveness of the control algorithm for active vibration control is demonstrated using MATLAB® simulations of a single-degree-of-freedom plant. This methodology provides superior performance in the presence of signi cant mistuning compared to a more conventional approach.
The standard curriculum for Aerospace Engineering students at the University of Cincinnati includes AEEM361 Integrated Aircraft Engineering. The goal of this course is to instruct students in the tools and methodology of aircraft design. The integrated aspects of aircraft design are underscored by introducing prejunior (between sophomore and junior) students to the state-of-the-art morphing technology, inspired by bat and bird flight, which can enable an aircraft to adapt its shape to best suit the flight condition thereby enhancing mission performance. In this article, we present the development of unique software tools, which provide undergraduates an opportunity to design airfoils for morphing aircraft. Morphing is introduced in the form of “on demand” camber as well as sweep change with the aim of improving aerodynamic efficiency for a multiobjective (several design points) mission profile. The Global Hawk UAV mission in general and its LRN1015 airfoil in particular is in focus due to the relative long mission times spent at the two different flight conditions, namely high-speed dash and low-speed loiter. We are using several tools to virtually simulate a morphing wing including XFOIL to perform fast and relatively accurate two-dimensional steady-flow simulations of different morphed configurations using a camber-controlled morphed wing to maneuver. In this article we detail AeroMorph, the educational MATLAB-based tool developed for design of a camber-controlled morphing of airfoils with the aim of improving aerodynamic efficiency and exploration of the basic relationships between flap deflection and airfoil morphing based on a camber change.
This work presents a methodology for real-time estimation of wildland fire growth, utilizing afire growth model based on a set of partial differential equations for prediction, and harnessing concepts of space-time Kalman filtering and Proper Orthogonal Decomposition techniques towards low dimensional estimation of potentially large spatio-temporal states. The estimation framework is discussed in its criticality towards potential applications such as forest fire surveillance with unmanned systems equipped with onboard sensor suites. The effectiveness of the estimation process is evaluated numerically over fire growth data simulated using a well-established fire growth model described by coupled partial differential equations. The methodology is shown to be fairly accurate in estimating spatio-temporal process states through noise-ridden measurements for real-time deploy ability.
This work presents a methodology for real-time estimation of wildland fire growth, utilizing a fire growth model based on a set of partial differential equations for prediction, and harnessing concepts of space-time Kalman filtering and Proper Orthogonal Decomposition techniques towards low dimensional estimation of potentially large spatio-temporal states. The estimation framework is discussed in its criticality towards potential applications such as forest fire surveillance with unmanned systems equipped with onboard sensor suites. The effectiveness of the estimation process is evaluated numerically over fire growth data simulated using a well-established fire growth model described by coupled partial differential equations. The methodology is shown to be fairly accurate in estimating spatio-temporal process states through noise-ridden measurements for real-time deployability.
The ability to spatially alter both the amount of body force along the span of a plasma actuator and the angle of the resulting jet relative to the surface has been demonstrated. A dielectric barrier discharge plasma actuator consists of two electrodes separated by a dielectric barrier, which imparts momentum to the surrounding fluid parallel to the dielectric. To investigate a technique to shape the spanwise body force created by the plasma actuator, a control volume momentum balance was used. By shaping the buried electrode along the span of the actuator, the local volume of plasma generated can be controlled, which is related to the local body force. Pressure measurements were taken in the boundary layer behind the actuator to calculate the momentum imparted to the flow at various spanwise locations corresponding to different electrode widths. Particle image velocimetry data were then used to show that spatially varying, steady jets could be created with the use of only one actuator by varying the width of the buried electrode in a quiescent flow. The angle of the jet created, relative to the dielectric, by a plasma synthetic jet is also investigated. By pointing two plasma actuators at each other, an inverted impinging jet can be created as a result of the two independent jets colliding. By altering the strength of one of the jets relative to the other, the angle of separation can be changed. Particle image velocimetry data were taken to show the effects of altering the voltage (strength) applied to one of the actuators relative to the other. It was found that, with this method, jet vectoring could be achieved. The angle of the jet could be controlled a full 180 deg through small changes in the voltage applied to the electrodes, also in a quiescent flow.
A genetic algorithm was used to optimize performance of a fuzzy inference system acting as a controller for a magnetically actuated CubeSat. A solely magnetically controlled satellite is a nonlinear, underactuated system for which the uncontrollable axis varies as a function of orbit position and attitude; variation is approximately periodic with orbit position. Therefore, controllability is not guaranteed, making solely magnetic control a less than ideal option for spacecraft requiring a high degree of pointing accuracy or spacecraft subject to relatively large disturbances. However, for small spacecraft, such as CubeSats, with modest pointing and disturbance rejection requirements, solely magnetic actuation is a good option. The genetic-algorithm-tuned fuzzy controller solution was compared to a similar linear quadratic regulator solution that was tuned to minimize the cost function used by the genetic algorithm. Both were optimized with respect to a single set of initial conditions. The genetic-algorithm-tuned fuzzy controller was found to be a lower-cost solution than the linear quadratic regulator for the optimized set of initial conditions. Additionally, a Monte Carlo analysis showed the genetic-algorithm-tuned fuzzy controller tended to settle faster than the linear quadratic regulator over a variety of initial conditions.
This research was conducted within the framework of a National Science Foundation sponsored summer Research Experience for Undergraduate (REU) students. This research considers small-scale and mathematical models of simple one-story structures that are subjected to free and base-motion excitations and installed with and without passive damping devices to gain an understanding of their dynamic behavior while reviewing active and semi-active damping means being applied and researched today. Using computer programming and numerical methods, the goal is to understand and counteract catastrophic disasters to structures caused by earthquakes. The research is broken down into a number of MATLAB simulations and experiments in order to understand basic dynamic and control features required to design earthquake resilient buildings. These experiments include free vibration experiments to test for the stiffness of columns for different heights and to test for the natural frequency and damping ratio of a one-story structure under different mass loads. Active PD control was then applied to an experimental system experiencing accelerations attributed to the Northridge 1994, Kobe 1995, El Centro 1940, and Mendocino 1992 earthquakes. Robustness comparisons were made between (1) P control; (2) D control; and (3) PD control for the above earthquake inputs to the shaker. A fuzzy logic controller was developed to effectively control transient vibrations. The uniqueness of this control concept is that the fuzzy control continuously varies the damping characteristics of a semi-active tuned mass damper (TMD). It was concluded that a fuzzy logic based TMD was more effective than a regular passive TMD, by providing half the settling times.
For a Timoshenko beam model the equations of motion, representing the anisotropic continuum model of a two-dimensional, latticed, large space structure, are extended to include coupling between the extensional, shear and bending modes. This analytical model, applied to a 20-bay, orthogonal, tetrahedral, cantilevered truss structure, is used to determine the transient response when subjected to a unit impulse. It is demonstrated that for beam-like structures having a fixed bending stiffness and beam mass an increase in diagonal stiffness, on account of the stiffness of the vertical girder, leads to a rise in the transverse shear rigidity. This results in higher natural frequencies and a reduction in peak displacement. In addition, in an asymmetrical truss configuration, coupling between the extensional and shear modes raises the maximum peak displacement compared to that obtained for a symmetric truss. The model is modified to investigate the introduction of passive damping in the form of several dynamic vibration absorbers. For a fixed absorber mass budget, a simple yet efficient absorber parameter optimization procedure, based on the classical steady state criteria of a 2-DOF system, is developed to design several absorbers each tuned to a different modal frequency. It is found that inclusion of transverse shear rigidity, as a design parameter in damping augmentation studies, reduces settling time for predetermined maximum peak displacements.
Fire is a natural component of many ecosystems but wildland fires often do pose serious threats to public safety, properties and natural resources. Forest fire acts as a dominant factor in reshaping of terrain and change of the ecosystem of a particular area. The total damage due to wildland fire shows an increasing trend over the past decade. Forest Fire Decision Support Systems (FFDSS) have been developed for the last thirty years all over the world that supplies valuable information on forest fire detection, fire behavior and other aspects of forest fires but lacks in developing intelligent fire suppression strategies. In this paper, an effort has been made to generate intelligent fire suppression strategies with efficient resource allocation using the Genetic Algorithm based optimization tool in a heterogeneous and uncertain scenario. The goal of this research is to perform intelligent resource allocation along with the generation of optimal firelines that minimizes the total burned area due to wildland fire. The solutions generated at each generations of the Genetic Algorithm (GA) are used to build the firelines in a heterogeneous terrain where advanced forest fire propagation model is used to evaluate the fitness values of each generated solutions. The optimal firelines thus obtained through the Simulation-Optimization technique minimizes the total damage due to wildland fire and eliminates the chance of any fire escape i.e., firefront reaching the fireline positions before they are built. Such techniques integrated with the existing FFDSS hold promise in effectively controlling forest fires.
The effect of feedback flow control on the wake of a circular cylinder at a Reynolds number of 100 is investigated in direct numerical simulation. The control approach uses a low-dimensional model based on proper orthogonal decomposition (POD). The controller applies linear proportional and differential feedback to the estimate of the first POD mode. The range of validity of the POD model is explored in detail. Actuation is implemented as displacement of the cylinder normal to the flow. It is demonstrated that the threshold peak amplitude below which the control actuation ceases to be effective is in the order of 5% of the cylinder diameter. The closed-loop feedback simulations explore the effect of both fixed-phase and variable-phase feedback on the wake. Whereas fixed-phase feedback is effective in reducing drag and unsteady lift, it fails to stabilize this state once the low drag state has been reached. Variable-phase feedback, however, achieves the same drag and unsteady lift reductions while being able to stabilize the flow in the low drag state. In the low drag state, the near wake is entirely steady, whereas the far wake exhibits vortex shedding at a reduced intensity. A drag reduction of 15% of the drag was achieved, and the unsteady lift force was lowered by 90%.
Feedback flow control of the wake of a circular cylinder at a Reynolds number of 100 is an interesting and challenging benchmark for controlling absolute instabilities associated with bluff body wakes. A two dimensional computational fluid dynamics simulation is used to develop low-dimensional models for estimator design. Actuation is implemented as displacement of the cylinder normal to the flow. The estimation approach uses a low dimensional model based on a truncated 6 mode Double Proper Orthogonal Decomposition (DPOD) applied to the streamwise velocity component of the flow field. Sensor placement is based on the intensity of the resulting spatial modes. A non-linear Artificial Neural Network Estimator (ANNE) was employed to map the velocity data to the mode amplitudes of the DPOD model. For a given four sensor configuration, developed using a previously validated strategy, ANNE performed better than two state-of-the-art approaches, namely, a Quadratic Stochastic Estimator (QSE) and a Linear Stochastic Estimator with time delays (DSE).
Mazes have intrigued the human mind for thousands of years, and have been used to measure cognitive abilities of laboratory animals. In recent years, mazes have been used to examine the artificial intelligence of robots by observing their ability to traverse mazes using algorithm for maze exploration and exploitation.A simulation of a multi-agent system is used to demonstrate the benefits of utilizing a group of several robots in maze exploration. Using a behavioral algorithm based on Tarry’s algorithm, it is shown that the group performance improves and becomes more robust as the number of robots increases. In addition, the amount of data transfer required for group coordination can be minimized to a small set of data items, which is independent of either the number of robots in the group or the maze size.As a result, the above multi-agent approach can be scaled up to mazes or groups of any size, as indicated by the results of the MATLAB-based simulation.
The present study deals with an AFCA (Adaptive Fuzzy Control Algorithm) for an Euler-Bernoulli approximation of a two-dimensional version of a cantilever beam-like orthogonal tetrahedral space truss. Transient disturbances, modeled as a unit impulse, excite all the modes of the beam. The resulting transverse displacement at the free end of the beam and its corresponding rate are observed by sensors placed there, and active control of the beam is provided by a collocated force actuator.
A design methodology for the closed-loop control algorithm that is independent of an exact mathematical model (space-state model, F.E.M., etc.) of plant dynamics and which is based on fuzzy logic is presented. First, the behavior of the open-loop system is observed. Then, the control force applied to the system emulates the behavior of a dynamic vibration absorber which is tuned to the measured fundamental frequency. This approach not only assures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The damping and the mass ratios of the absorber adapt themselves by using a fuzzy decision-making process. This results in relatively quick settling times, low overshoots and dying out of vibration within a few seconds.
When the control force is turned off after a mere 16 seconds, almost all the vibrational energy is dissipated. In addition, the performance of the AFCA is insensitive to varying initial conditions. To demonstrate the robustness of the control system to changes in the temporal dynamics of the cantilever beam, the transient response to a considerably perturbed plant is simulated. The Young's modulus of the beam was raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. The mode shapes, however, remain unchanged. For these cases, too, the AFCA provides similar settling times and rates of vibrational energy dissipation.
This paper describes a market-based solution to the problem of assigning mobile agents to tasks. The problem is formulated as the multiple depots, multiple traveling salesmen problem (MTSP), where agents and tasks operate in a market to achieve near-optimal solutions. We consider both the classical MTSP, in which the sum of all tour lengths is minimized, and the Min-Max MTSP, in which the longest tour is minimized. We compare the market-based solution with direct enumeration in small scenarios, and show that the results are nearly optimal. For the classical MTSP, we compare our results to linear programming, and show that the results are within 1 % of the best cost found by linear programming in more than 90 % of the runs, with a significant reduction in runtime. For the Min-Max case, we compare our method with Carlsson's algorithm and show an improvement of 5 % to 40 % in cost, albeit at an increase in runtime. Finally, we demonstrate the ability of the market-based solution to deal with changes in the scenario, e.g., agents leaving and entering the market. We show that the market paradigm is ideal for dealing with these changes during runtime, without the need to restart the algorithm, and that the solution reacts to the new scenarios in a quick and near-optimal way.
Tasks allocation is a fundamental problem in multiagent systems. We formulate the problem as a multiple traveling salesmen problem (MTSP), which is an extension to the well known traveling salesman problem (TSP), both considered to be NP-hard combinatorial optimization problems. We propose a solution in which agents interact in an economic market to win tasks situated in an environment. The agents strive to minimize required costs, defined as either the total distance traveled by all agents or the maximum distance traveled by any agent. Using a set of simple market operations, the agents come up with a solution for task allocation. In this work we examine the processing speed of the market-based solution (MBS), as well as the quality vs. optimal solutions achieved using enumeration for a 3 agents by 8 tasks scenario. We show that the MBS is both quick and close to optimal. We then show that the MBS can be scaled to more complicated problems, by comparing its results with results from genetic algorithm (GA) and clustering. We also show the robustness of the MBS to changes in the scenario, e.g. the addition and removal of tasks or agents.
For the systematic development of feedback flow controllers, a numerical model that captures the dynamic behaviour of the flow field to be controlled is required. This poses a particular challenge for flow fields where the dynamic behaviour is nonlinear, and the governing equations cannot easily be solved in closed form. This has led to many versions of low-dimensional modelling techniques, which we extend in this work to represent better the impact of actuation on the flow. For the benchmark problem of a circular cylinder wake in the laminar regime, we introduce a novel extension to the proper orthogonal decomposition (POD) procedure that facilitates mode construction from transient data sets. We demonstrate the performance of this new decomposition by applying it to a data set from the development of the limit cycle oscillation of a circular cylinder wake simulation as well as an ensemble of transient forced simulation results. The modes obtained from this decomposition, which we refer to as the double POD (DPOD) method, correctly track the changes of the spatial modes both during the evolution of the limit cycle and when forcing is applied by transverse translation of the cylinder. The mode amplitudes, which are obtained by projecting the original data sets onto the truncated DPOD modes, can be used to construct a dynamic mathematical model of the wake that accurately predicts the wake flow dynamics within the lock-in region at low forcing amplitudes. This low dimensional model, derived using nonlinear artificial neural network based system identification methods, is robust and accurate and can be used to simulate the dynamic behaviour of the wake flow. We demonstrate this ability not just for unforced and open-loop forced data, but also for a feedback-controlled simulation that leads to a 90% reduction in lift fluctuations. This indicates the possibility of constructing accurate dynamic low-dimensional models for feedback control by using unforced and transient forced data only.
The methods and outcome of a senior undergraduate project related to the control of a turbulent cylinder wake flow using plasma actuators are summarized in this article. The study integrates computational fluid dynamics (CFD) with experimentation and combines fluid mechanics with flow control research, crossing the boundaries between engineering disciplines.Comput. Appl. Eng. Educ.
The problem of assigning a group of Unmanned Aerial Vehicles (UAVs) to perform spatially distributed tasks often requires that the tasks will be performed as quickly as possible. This problem can be defined as the Min–Max Multiple Depots Vehicle Routing Problem (MMMDVRP), which is a benchmark combinatorial optimization problem. In this problem, UAVs are assigned to service tasks so that each task is serviced once and the goal is to minimize the longest tour performed by any UAV in its motion from its initial location (depot) to the tasks and back to the depot. This problem arises in many time-critical applications, e.g. mobile targets assigned to UAVs in a military context, wildfire fighting, and disaster relief efforts in civilian applications. In this work, we formulate the problem using Mixed Integer Linear Programming (MILP) and Binary Programming and show the scalability limitation of these formulations. To improve scalability, we propose a hierarchical market-based solution (MBS). Simulation results demonstrate the ability of the MBS to solve large scale problems and obtain better costs compared with other known heuristic solution.
The effectiveness of a sensor configuration for feedback flow control on the wake of a circular cylinder is investigated in both direct numerical simulation as well as in a water tunnel experiment. The research program is aimed at suppressing the von Kármán vortex street in the wake of a cylinder at a Reynolds number of 100. The design of sensor number and placement was based on data from a laminar two-dimensional simulation of the Navier–Stokes equations for the unforced condition. A low-dimensional proper orthogonal decomposition (POD) was applied to the vorticity calculated from the flow field and sensor placement was based on the intensity of the resulting spatial eigenfunctions. The numerically generated data was comprised of 70 snapshots taken over three cycles from the steady state regime. A linear stochastic estimator (LSE) was employed to map the velocity data to the temporal coefficients of the reduced order model. The capability of the sensor configuration to provide accurate estimates of the four low-dimensional states was validated experimentally in a water tunnel at a Reynolds number of 108. For the experimental wake, a sample of 200 particle image velocimetry (PIV) measurements was used. Results show that for experimental data, the root mean square estimation error of the estimates of the first two modes was within 6% of the desired values and for the next two modes was within 20% of the desired values. This level of error is acceptable for a moderately robust controller.