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.
Fuzzy logic is used in a variety of applications because of its universal approximator attribute and non-linear characteristics. But, it takes a lot of trial and error to come up with a set of membership functions and rule-base that will effectively work for a specific application. This process could be simplified by using a heuristic search algorithm like Genetic Algorithm (GA). In this paper, genetic fuzzy is applied to the task assignment for cooperating Unmanned Aerial Vehicles (UAVs) classified as the polygon visiting multiple traveling salesman problem (PVMTSP). The PVMTSP finds a lot of applications including UAV swarm routing. We propose a method of genetic fuzzy clustering that would be specific to PVMTSP problems and hence more efficient compared to k-means and c-means clustering. We developed two different algorithms using genetic fuzzy. One evaluates the distance covered by each UAV to cluster the search-space and the other uses a cost function that approximates the distance covered thus resulting in a reduced computational time. We compare these two approaches to each other as well as to an already benchmarked fuzzy clustering algorithm which is the current state-of-the-art. We also discuss how well our algorithm scales for increasing number of targets. The results are compared for small and large polygon sizes.
UAV’s are being increasingly used today than ever before in both military and civil applications. A certain level of autonomy is imperative to the future of UAV’s. A quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAV’s are VTOL and hovering capabilities as well as a greater maneuverability. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. The objective of this research endeavor is to present the steps of designing, building and simulating an intelligent flight control module for a quadrotor UAV. Validation of the math model developed is discussed using actual flight data. Excellent attitude tracking is demonstrated for near hover flight regimes. System design is comprehensively dealt with. The responses are analyzed and future work involving hardware-in-the-loop simulations is proposed.
Wildfire is one of the most significant disturbances responsible for reshaping the terrain and changing the ecosystem of a particular region. Its detrimental effects on environment as well as human lives and properties, and growing trend in terms of frequency and intensity of wildfires over the last decade have necessitated the development of efficient forest fire management techniques. During the last three decades, Forest Fire Decision Support Systems (FFDSS) have been developed to help in the decision-making processes during forest fires by providing necessary information on fire detection, their status and behavior, and other aspects of forest fires. However, most of these decision support systems lack the capability of developing intelligent fire suppression strategies based upon current status and predicted behavior of forest fire. This paper presents an approach for development of efficient fireline building strategies via intelligent resource allocation. A Genetic Algorithm based approach has been proposed in this paper for resource allocation and optimum fireline building that minimizes the total damage due to wildland fires. The approach is based on a simulation–optimization technique in which the Genetic Algorithm uses advanced forest fire propagation models based upon Huygens principles for evaluation of cost index of its solutions. Both homogeneous and heterogeneous environmental conditions have been considered. Uncertainties in weather conditions as well as imperfect knowledge about exact vegetation and topographical conditions make exact prediction of wildfires very difficult. The paper incorporates Monte-Carlo simulations to develop robust strategies in uncertain conditions. Extensive simulations demonstrate the effectiveness of the proposed approach in efficient resource allocation for fighting
complex wildfires in uncertain and dynamic conditions.
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.
Over the past 10 years there has been a growing need to introduce closed-loop control technology for vibration suppression of buildings subject to wind or earthquake disturbances. This paper deals with the investigation of the effectiveness of a fuzzy logic based time variable damping tuned mass damper (TMD) on a building structure undergoing free and forced vibrations. The uniqueness of this approach is the application of a robust, nonlinear fuzzy based controller to emulate a time-optimal control strategy. Fuzzy logic based time variable damping is introduced into a semi-active TMD in order to enhance its performance in the vibration suppression of buildings. First, a single story structure for three different vibration suppression approaches is studied. The fuzzy logic based time variable damping TMD (fuzzy TMD) is compared to the baseline passive TMD as well as a conventional proportional-derivative (PD) controller. Forced vibration is introduced using a resonant harmonic sinusoidal excitation (i.e. same frequency as the fundamental frequency of the structure). Finally, the fuzzy TMD is compared to the baseline for the free vibration of a 15 story structure. For both structures studied, MATLAB based simulation results show that the passive TMD and the PD, both constant gain approaches, provide similar results whereas the fuzzy TMD yields half the settling time. This effort clearly demonstrates the potential of a variable gain (damping) strategy for the vibration suppression of buildings.
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.
There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV) being capable ofmaneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamicmotion planning algorithm, UAVs would be able tomaneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent’s sensing range) and target location and outputs a change in heading angle and speed. The FL controller was validated, andMonte Carlo testing was completed to evaluate the performance.Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC) feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF) solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.
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).
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.