الفهرس 
Chapter 1 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
In this thesis, an adaptive model predictive controller (AMPC) is studied as a solution for the path tracking control problem for autonomous vehicles. The effect of feeding the MPC with a continuously changing vehicle’s mathematical model is studied so that the controller becomes more adaptable to changing parameter values accompanied by the instantaneous states. The proposed approach is compared with both the Stanley controller and a similar MPC that uses a fixed vehicle model. The performance is measured by the ability to minimize both lateral position and heading angle errors. A dynamic bicycle model for the vehicle is deployed in the MPC, and the controllers are simulated in CarSim-MATLAB/Simulink co-simulation environment using three known maneuvers: S-Road, double lane change and curved road. The results and comparison expand to use the Hardware in the Loop -HIL- simulation technique to make sure that the behavior of the controller is not affected by the presence of a real hardware actuator with its low-level control method, the obtained HIL results are compared with a Fuzzy Predictive Stanley -FPS- controller. Results show that the proposed controller gives better tracking performance than the three other competitors with minimal maximum, root mean square -RMS-, and mean absolute -MA- errors.