Matilde Santos Peñas

Particle swarm optimisation of interplanetary trajectories from Earth to Jupiter and Saturn

One of the most fascinating aerospace problems today is the optimisation of interplanetary trajectories in the Solar System, using gravitational fly-bys in order to reduce fuel consumption and/or mission duration. This is a complex optimisation problem due to the complexity of the models and the lack of analytical solutions, as well as to the presence of strong discontinuities. An exhaustive search of the space of input variables is unaffordable even with modern, state-of-the-art computing technology. Thus, a feasible approach requires artificial intelligence and modern optimisation techniques based on the intelligent selection of some potential solutions. These individuals evolve in order to generate better solutions until some optimisation criteria are met. In this work, two evolutive algorithms are considered, both based on particle swarm optimisation. In both cases, the trajectory to be optimised departs from Earth and, after a fly-by in Mars, arrives to Jupiter. In the single-criteria case, only the fuel consumption is considered as a variable to be minimised, while in the multi-criteria case the fuel consumption and the total time of the mission are simultaneously taken into account, building a Pareto-front of non-dominated solutions that provides an interesting view of the possible options for the space mission. In both, the single- and multi-criteria cases, the swarm algorithms optimise some tuning parameters of the trajectory: launch epoch, and times of flight between planets. Results are compared to other works on the same problem. They demonstrate the benefit of applying these evolutive techniques to decrease both mission duration and propellant cost when using intermediate gravity assist bodies.

Regulation of carnitine palmitoyltransferase activity in the liver and brown adipose tissue in the newborn rat: Effect of starvation and hypothermia

The overt activity of hepatic carnitine palmitoyltransferase (CPT1) increased during the last day of gestation in the foetus and after prolonged starvation in the newborn kept at 37 degrees C. Its sensitivity to inhibition by malonyl-CoA decreased during the perinatal period studied. Brown fat CPT1 increased under the same experimental conditions. However, its sensitivity to malonyl-CoA remains unchanged. Hypothermia at 24 degrees C decreased in the liver and increased in brown adipose tissue CPT1 activity in response to fasting. Glucose injection at birth decreased CPT1 activity in the liver but did not have any effect in the presence of mannoheptulose. This effect of glucose was non-significant in brown adipose tissue.

Multi-criteria genetic optimisation of the manoeuvres of a two-stage launcher

The aim of this paper is to study the use of a genetic algorithm (GA) to optimise the ascent trajectory of a conventional two-stage launcher. The equations of motion of this system lack analytical solutions, and the number of adjustable parameters is large enough that the use of some non-traditional optimisation method becomes necessary. Two different missions are considered: first, to reach the highest possible stable, circular Low Earth Orbit (LEO); and second, to maximise the speed of a tangential escape trajectory. In this study, three variables are tuned and optimised by the GA in order to satisfy mission constraints while maximising the target function. The technical characteristics and limitations of the launcher are taken into account in the mission model, and a fixed payload weight is assumed. A variable mutation rate helps expand the search area whenever the population of solutions becomes uniform, and is shown to accelerate convergence of the GA in both cases. The obtained results are in agreement with technical specifications and solutions obtained in the past.

Fuzzy logic steering control of autonomous vehicles inside roundabouts

Graphical abstractDisplay Omitted HighlightsThis paper shows a real experiment with autonomous vehicles in urban roundabouts.A parametric curve generation in used in the path planning, which in divided in three stages: entrance, inside and exit of the roundabout.Lane changes experiments show that the controller proposed (based on fuzzy logic) is stable.The range of speed used in the experiment is between 8km/h and 24km/h. The system was designed for 20km/h max.The real situation experiments show that our proposal is valid for urban scenarios. The expansion of roads and the development of new road infrastructures have increased in recent years, linked to the population growing in large cities. In the last two decades, roundabouts have largely replaced traditional intersections in many countries. They have the advantage of allowing drivers continuous flow when traffic is clear, without the usual delay caused by traffic lights. Although roundabouts with and without traffic-signal control have been widely used and considered in the literature, driverless control on roundabouts has not been studied in depth yet. The behavior of autonomous vehicles in roundabouts can be divided into three stages: entrance, inside, and exit. The first and last may be handled as an extension of intersections. However, autonomous driving on the roundabout requires special attention. In this paper, the design and implementation of a fuzzy logic system for the steering control of autonomous vehicles inside the roundabout is proposed. Cascade architecture for lateral control and parametric trajectory generation are used. Fuzzy control has proved to be easy to define using expert knowledge. Experiments with a real prototype have been carried out, taking into account different speed profiles and lane change maneuvers inside the roundabout, with very satisfactory results.

Parameter selection based on fuzzy logic to improve UAV path-following algorithms

Abstract In order to steer an Unmanned Aerial Vehicle (UAV) and make it follow a desired trajectory, a high level controller is needed. Depending on the control algorithm, one or more parameters have to be tuned, having their values high impact on the performance. In most of the works, these parameters are taken as constant. In this paper, we apply fuzzy logic to select the parameters of the control law and compare this approach with the tuning by constant parameters and with another adjusting method based on the kinematic analysis of the equations of the UAV. After many simulations of the quadrotor following randomly generated paths, we have proved that the fuzzy tuning law is not only a good and feasible solution, but also more general as it can be applied to any trajectory.

Negative Selection and Knuth Morris Pratt Algorithm for Anomaly Detection

In this paper an algorithm for detecting anomalous behavior on computer systems is proposed. The work is based on information from the behavior of authorized users who have performed various tasks on a computer system over two years. The study uses a dynamic data structure that can encode the current activities of users and their behaviors. The identification of the most and least frequent tasks, based on the historical database of each user, provides a simple way of creating a single profile of behavior. With this profile, we apply negative selection techniques to obtain a reasonable computational size set of anomalous detectors. We then apply the Knuth-Morris-Pratt algorithm for locating detectors of anomalies as indicators of fraudulent behavior. This procedure for detecting anomalous behavior has been tested on real data and the results prove the effectiveness of the proposal and motivate further research to improve the existing detection system.

Delta-V genetic optimisation of a trajectory from Earth to Saturn with fly-by in Mars

The aim of this article is to analyse the results obtained when using a genetic algorithm (GA) to optimise the interplanetary trajectory of a spacecraft. The desired trajectory should visit Saturn, after performing a gravitational assistance or fly-by in planet Mars. The GA tunes a set of variables, in order to achieve the mission purpose while satisfying the constraints and minimizing the delta-V of the mission. Due to the complexity of the implemented models and the lack of analytical solutions, an alternative non-traditional algorithm provided by soft-computing techniques such as GA is necessary to achieve an optimum solution. The positions of planets as provided by Jet Propulsion Laboratory have been considered. A variable mutation rate has been implemented that broadens the search area whenever the population becomes uniform. The results are very useful from the point of view of mission analysis and therefore can be used as an initial guess for further optimizations. They can also be the first step for a more refined analysis and time-consuming simulations based on more complex models of orbital perturbations.

A Fuzzy Decision System for an Autonomous Car Parking

In this paper, the design of a fuzzy decision system for autonomous parallel car parking is presented. The modeling of the problem, the physics involved, the fuzzy sets assigned to the linguistic variables and the inference rules are explained. Different fuzzy operators have been tested in the generated simulation environment and the results have been compared. The decision system is proposed as a benchmark to show the influence of the different fuzzy strategies on the final decision. The comparison is made in terms of number of maneuvers for parking the vehicle. It also depends on the range of measurements to environmental objects around the car and on the starting position. The final implementation of the parking system in a Java Applet can be tested in the web using any browser.

Intelligent satellites control based on fuzzy logic in the Earth-Moon Libration points

One of the most challenging issues in astrodynamic is the control of the satellites, in order to achieve a certain trajectory or to maintain some specific position with respect to a planet or to other spacecrafts. An interesting and quite innovative problem is the control of satellites around the five Lagrangian points of a binary system, using continuous thrust. In this work, using a complex and realistic model of the binary system, intelligent systems based on fuzzy logic are used to control the satellites in the Earth-Moon Libration points. The fuzzy controllers have been compared to conventional ones and it has been proved how this intelligent strategy reduces the control effort and therefore the fuel budget.

Reaching a consensus on access detection by a decision system

Classification techniques based on Artificial Intelligence are computational tools that have been applied to detection of intrusions (IDS) with encouraging results. They are able to solve problems related to information security in an efficient way. The intrusion detection implies the use of huge amount of information. For this reason heuristic methodologies have been proposed. In this paper, decision trees, Naive Bayes, and supervised classifying systems UCS, are combined to improve the performance of a classifier. In order to validate the system, a scenario based on real data of the NSL-KDD99 dataset is used.