APPRAISAL OF TAKAGI–SUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics

Deep reinforcement learning vehicle longitudinal control

[Resumen] Este artículo presenta un sistema inteligente para el control longitudinal de un vehículo mediante aprendizaje por refuerzo profundo basado en la influencia de la curvatura de la carretera. El sistema inteligente consiste en un agente que usa el algoritmo de gradiente de política determinista profundo (DDPG) para controlar la velocidad. Para entrenar al agente del modelo, el efecto de la curvatura de la carretera se considera a través de la aceleración percibida obtenida a partir de la aceleración lateral y velocidad angular debida a la propia carretera. Los resultados del sistema inteligente son valores continuos de aceleración. El modelo propuesto ofrece resultados prometedores, lo que sugiere que este sistema inteligente puede ayudar al conductor y que el sistema de control del vehículo puede aplicarse a la conducción semiautónoma o autónoma haciendo que la conducción sea más segura y cómoda.[Abstract] This paper presents an intelligent system for longitudinal control of a vehicle using deep reinforcement learning based on the influence of road curvature. The intelligent system consists of an agent based on the deep deterministic policy gradient (DDPG) algorithm for speed control. To train the model agent, the road curvature effect is considered through the perceived acceleration obtained from the lateral acceleration and angular velocity due to the road itself. The results of the intelligent system are continuous acceleration values. The proposed model offers promising results, suggesting that this intelligent system can assist the driver and that the vehicle control system can be applied to semi-autonomous or autonomous driving making driving safer and more comfortable

Controladores borrosos para la dirección de vehículos autónomos en maniobras dentro de entornos urbanos

National audienceHasta la fecha, los sistemas de ayuda a la conducción desarrollados en el sector de la automoción se centran especialmente en el control de velocidad del vehículo. Sin embargo, sistemas que involucren el control (ya sea parcial o total) sobre la dirección del vehículo se encuentran todavía en fase experimental. Este trabajo está centrado en el diseño, desarrollo e implementación de un sistema de control lateral en cascada para vehículos autónomos reales, basado en controladores borrosos de alto nivel para maniobras en circuitos urbanos. Diferentes experimentos se han llevado a cabo en curvas de distinto radio y a diferentes velocidades (dentro de entornos urbanos), además, se han implementado dos nuevas maniobras: la marcha atrás y conducción en rotondas, mostrando un buen funcionamiento

Design and implementation of Adaptive Neuro-Fuzzy Inference system for the control of an uncertain Ball on Beam Apparatus

Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro-Fuzzy Inference System) using an uncertain Ball and Beam System. The custom-designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle

A driverless vehicle demonstration on motorways and in urban environments

International audienceThe constant growth of the number of vehicles in today's world demands improvements in the safety and efficiency of roads and road use. This can be in part satisfied by the implementation of autonomous driving systems because of their greater precision than human drivers in controlling a vehicle. As result, the capacity of the roads would be increased by reducing the spacing between vehicles. Moreover, greener driving modes could be applied so that the fuel consumption, and therefore carbon emissions, would be reduced. This paper presents the results obtained by the AUTOPIA program during a public demonstration performed in June 2012. This driverless experiment consisted of a 100-kilometre route around Madrid (Spain), including both urban and motorway environments. A first vehicle – acting as leader and manually driven – transmitted its relevant information – i.e., position and speed – through an 802.11p communication link to a second vehicle, which tracked the leader's trajectory and speed while maintaining a safe distance. The results were encouraging, and showed the viability of the AUTOPIA approach

Predictive energy-efficient motion trajectory optimization of electric vehicles

This work uses a combination of existing and novel methods to optimize the motion trajectory of an electric vehicle in order to improve the energy efficiency and other criteria for a predefined route. The optimization uses a single combined cost function incorporating energy efficiency, travel safety, physical feasibility, and other criteria. Another focus is the optimal behavior beyond the regular optimization horizon

Design and Implementation of a Neuro-Fuzzy System for Longitudinal Control of Autonomous Vehicles

International audienceThe control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the longitudinal control (throttle and brake management) is an important topic because external perturbations can generate uncomfortable accelerations as well as unnecessary fuel consumption. In this work, we utilize a neuro-fuzzy system to use human driving knowledge to tune and adjust the input-output parameters of a fuzzy ifthen system. The neuro-fuzzy system considered in this work is ANFIS (Adaptive-Network-based Fuzzy Inference System). Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous manual tuned controller, mainly in comfort and efficient use of actuators