María Jesús López Boada

Fuzzy-logic applied to yaw moment control for vehicle stability

In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that the vehicle follows the target values of the yaw rate and the sideslip angle. The simulation results show the effectiveness of the proposed control method when the vehicle is subjected to different cornering steering manoeuvres such as change line and J-turn under different driving conditions (dry road and snow-covered).

A perception system based on laser information for mobile robot topologic navigation

This paper presents a perception system for topological navigation using laser information. The localization system is based on the detection of landmarks (walls, doors and corners). A search algorithm based on Hough transform techniques for pattern recognition is used. The developed system allows the topologic localization and navigation of a mobile robot using landmarks. The resulting self-localization module has been integrated successfully in a more complicated navigation system. Various experimental results show the effectiveness of the presented algorithm.

Visual approach skill for a mobile robot using learning and fusion of simple skills

Abstract This paper presents a reinforcement learning algorithm which allows a robot, with a single camera mounted on a pan tilt platform, to learn simple skills such as watch and orientation and to obtain the complex skill called approach combining the previously learned ones. The reinforcement signal the robot receives is a real continuous value so it is not necessary to estimate an expected reward. Skills are implemented with a generic structure which permits complex skill creation from sequencing, output addition and data flow of available simple skills.

Active roll control using reinforcement learning for a single unit heavy vehicle

In this paper, a reinforcement learning algorithm using neural networks to improve the roll stability in a single unit heavy vehicle is proposed. The controller, consisting of active anti-roll bars, provides the adequate roll moments to the vehicle to improve its performance. The main advantages of the implemented neuro control are its good performance to control non-linear systems, it is a free-model control and it learns online, so that the system can adapt to changes produced in the environment. In this case, it is only necessary to measure a unique variable (the sprung mass roll angle) to control the vehicle so both the number of sensors and vehicle cost are reduced. Simulation results show the effectiveness of the proposed control system during different manoeuvres such as J-turn and lane-change.

Self-generation by a mobile robot of topological maps of corridors

In this paper a system for generation of topological maps is presented. This system is considered as one of the deliberative skills of the mobile robots architecture named AD. AD is a two level architecture: deliberative and automatic. Those skills which require high computational time as consequence of high level reasoning are found in the deliberative level, while the automatic level skills interact with robot sensors and actuators. The topological map generated with this deliberative skill is the map belonging to the EDN navigation system, and is named Navigation Chart. In the Navigation Chart, the information obtained from the chart is stored as nodes and as edges. Nodes correspond to the sensorial events and edges correspond to the sensorimotor skills.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

SIMPERF: SIMULINK‐based educational software for vehicle's performance estimation

This paper presents an educational software called SIMPERF developed to allow the engineering students to learn easily and quickly about the vehicles performance calculations. This software uses the SIMULINK library of MATLAB which has shown to be a good choice to implement and solve the implicated equations. The model allows us to achieve the vehicles performances with enough accuracy and to modify the parameters than influence on these performances quickly and easily in order to understand the physic phenomena involved.

Dynamic Performance Analysis of a Light Van Body-In-White Structure:

Requirement for low emissions and better vehicle performance has led to the demand for lightweight vehicle structures. Lighter gauge panels are being used to construct the body-in-white (BIW) monocoque structure, which is the basic component of the vehicle body. Since lighter gauge panels tend to generate more vibration and interior noise, it is necessary to optimize the dynamic performance of lightweight vehicle structures in order to achieve acceptable levels of vibro-acoustic performance. The design of a light commercial van structure has evolved over the years and through a lightweighting exercise the current BIW is about 10 per cent lighter than the previous BIW even though the volume capacity was increased by 15 per cent and the load-carrying capacity by 18 per cent. In this study, the dynamic performance of the current production light van BIW structure is investigated. Its performance is assessed against the structural dynamic performance standards which have been established for this class of structures. While the input mobility performance was found to exceed the standards easily, the modal mobility performance was found to be unsatisfactory owing to the occurrence of local panel resonant modes in the two side panels. A finite element model of the structure was developed to study the effect of adding stringers to the roof and side panels to eliminate some of the local panel modes and thus to improve the dynamic performance of the structure.

Simplified dynamic torsional model of an urban bus

Nowadays, one of the concerns of vehicle safety is the bus rollover due to the great number of injured and deaths. The torsion stiffness of the bus structure is a parameter of security against rollover. The torsion behaviour of the bus structure is studied from a dynamic point of view. In this paper, a dynamic free vibration model, based on the Holzer method, of a bus body is proposed. Modes and natural vibration frequencies have been obtained applying the proposed model and compared with those calculated by means of a Finite Element Model (FEM) of the bus structure, which has been validated by experimental static torsion tests. Results show that the natural frequencies and modes using the proposed dynamic model agree with those obtained with the FEM.

A New Non-Parametric Model Based on Neural Network for a MR Damper

Currently dampers based on magnetorheological (MR) fluids are being used in many applications such as construction, biomechanical and semi-active suspension to improve their behaviour. The main advantage of MR dampers is its very low time response (≈ 10 ms). In many cases, it is necessary to establish a suitable model of MR damper which characterizes its behaviour so that this model can be used in the simulation stage. In this paper, a new non-parametric model is proposed based on neural networks using a recursive lazy learning to model the MR damper behaviour. The proposed method is validated by comparison with experimental obtained responses. Results show that the estimated model correlates very well with the data obtained experimentally and learns quickly.Copyright