ACC+Stop&Go Maneuvers With Throttle and Brake Fuzzy Control (original) (raw)
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Adaptive Cruise Control using Fuzzy Logic
In recent years many studies on intelligent vehicles have been devoted to solve problem such as accident prevention, traffic flow smoothing. Adaptive Cruise Control (ACC) is used to maintain a constant safe distance between the host vehicle and the leading vehicle to avoid rear end collisions. It is an automotive feature that allows the speed of the vehicle to adapt to the traffic environment. ACC operates in distance control mode and velocity control mode. The method by which the ACC vehicle’s speed is controlled is via engine throttle control and limited brake operation. The inter-vehicular distance between the vehicles is measured. Desired speed is obtained from the distance measured. Neural Network and fuzzy logic Controller is trained to produce the desired acceleration and braking. In this research, ACC is implemented using the comparative analysis of Neural Network and Fuzzy Algorithm. The results demonstrate that for every parameter the proposed architecture outrages the conventional Neural Networks. The model is developed on MATLAB platform and comparisons were made based on evaluation parameters.
Adaptive Fuzzy Cruise Control System to Maintain Safe Distance and Set Speed
2019
— This paper presents an Adaptive Fuzzy Cruise Control (AFCC) system. The AFCC is modeled, designed and tested using MATLAB/SIMULINK. The presented AFCC gives numerous important points to the driver. It gives the driver the controllability of speeding intelligently. The proposed AFCC is simpler for a driver to drive in various driving conditions. It gives the driver a commanding on safety as well in various driving conditions.
Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles
2010
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 if-then 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.
2012
This paper focuses on the design of adaptive cruise control (ACC) which was implemented on a passenger car based on sliding mode control (SMC) of throttle valve combining with fuzzy logic control of brake pedal. An important feature of the new adaptive cruise control system is the ability to maintain a proper inter-vehicle gap based on the speed of host vehicle and headway way. There are three important inputs of the ACC system, speed of host vehicle read from electronic control unit (ECU), headway time set by driver, and actual gap measured from a laser scanner. The ACC processes these three inputs in order to calculate distance error and relative velocity which are used as the two inputs for both SMC and fuzzy controller. The SMC determines throttle valve angle while fuzzy controller determines the brake command to maintain a proper gap based on current speed of the leading vehicle and the desired time headway. The experimental results show that the proposed controller can perform...