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Neural Network based Controller for High Speed Vehicle following Predetermined Path

Yaw, Tan Zhang (2006) Neural Network based Controller for High Speed Vehicle following Predetermined Path. Universiti Teknologi Petronas. (Unpublished)

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The actual integration of automated control systems in vehicles such as Anti-lock Braking Systems (ABS) or Traction Control System (TCS) has proved to increase road safety and improve driver's comfort. Since most of the accidents are attributed to the fault of the driver, automated control systems in vehicle safety technology may dramatically better road safety by improving driver's performance. This thesis presents an enhanced and improved autonomous intelligent cruise control systems with obstacle collision avoidance integrated with path following/lane keeping. Obstacle collision avoidance is the ability to avoid obstaclesthat are in the vehicle's path, without causing damage to the obstacle or vehicle. Path following/lane keeping is the ability to follow the vehicle's path and keeping in its lane, as accurately as possible. The idea is to have a vehicle that drives by itself and avoids obstacles in the real world. Every instant, the vehicle decides by itself how to modify its direction according to its environment. This thesis demonstrates Gaussian functions and multi-objective cost function employed alongside with the Neural Network and optimal preview controller for control of the position of the vehicle to move while avoiding collision with obstacles. Each obstacle is represented independent of the others as a bell-shaped hump by the Gaussian functions which serve as an obstacle recognition system. Multi-objective cost function is formed for the planning strategy to generate, evaluate and select plans so that the vehicle can select which direction to move. Neural Network and optimal preview steering control are utilized to control a full linear steering model of a vehicle so as to increase path following accuracy. Optimal preview control is capable to portray the driver's vision of the path and process the knowledge while Neural Network controller has the ability to 'learn' from past errors and adjust the network to obtain specific target output. In this thesis, a MATLAB simulation environment was created to simulate the ability of a vehicle to avoid obstacles that are in the vehicle's path. Simulated obstacle avoidance has confirmed the capability of a vehicle to precisely avoid collision with obstacles while traveling on high speed along its predetermined path. in

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - System on Chip (SoC)
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 30 Sep 2013 16:30
Last Modified: 25 Jan 2017 09:46
URI: http://utpedia.utp.edu.my/id/eprint/7232

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