Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles

Abstract

Abstract-The 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.

Low-Speed Longitudinal Controllers for Mass-Produced Cars: A Comparative Study

“…There has been a recent application to improve speed tracking and the comfort of the vehicle in a simulation [25]. Also, some of the latest work of our group [26] has shown that previous fuzzy controllers can be improved by incorporating the experience of expert drivers via a neuro-fuzzy system, but, although the driving achieved was more comfortable, there was still a large speed reference error (around 1.59 km/h) [26]. The controllers described above in the present work-the i-PI and fuzzy controllers-provide the basis on which to generate a new controller using neuro-fuzzy techniques.…”

Appraisal of Takagi–sugeno Type Neuro-Fuzzy Network System With a Modified Differential Evolution Method to Predict Nonlinear Wheel Dynamics Caused by Road Irregularities

“…It should be divulged that the proposed Takagi-Sugeno type system modified by DE model can be embedded as a module with computa-tionally efficiency and adaptability. Neuro-fuzzy system has shown a reasonable applicability for longitudinal control of autonomous vehicles putting this method as a computationally efficient approach to solve vehicle dynamics related fields (Pérez et al 2010). A comparison was made between a non-fuzzy approach and ANFIS model using a very well documented study with finite element method approach.…”

Application of metaheuristic control strategies to voltage regulation

“…The hybrid ANFIS controller was first introduced in 1993 by Jang [4] by combining a fuzzy interface system (FIS) with the learning capability of artificial neural networks (ANN) [5]. ANFIS has previously been applied in many fields such as speed controller of electro-mechanical system [6], control of autonomous vehicles [7], Voltage and Frequency Stability in Islanded Microgrids [5] and also in Power Flow Analysis and Control [8,9].…”