In the past few years the development of technology made possible to perform algorithms demanding more and more computing capacity on embedded systems in a short time. Therefore, revolutionary innovations were released in the vehicle industry, and during the developments the cars became more autonomous.
Most of the complex computing tasks perform a control algorithm. These are very useful processes, since the controlling unit of the car is able to react much faster than the driver in certain situations. Regarding safety and comfort of the passangers, this is vital. One of the most important safety system in a car is the ABS (Anti-Lock Braking System). With the ABS system, the car remains steerable, and the brake distance is also reduced in case of a strong braking.
During the controlling process of ABS, the goal is to control the velocity of the wheels compared to the longitudinal speed of the car to the extent that the friction coeffient between the wheel and the surface of the road is maximal. In my thesis I have created the model of the car and the road surface, and I have examined different control algorithms. I have implemented a model predictive controller, which is able to determine the ideal braking force by knowing the torque of the wheel. After the MPC, I have implemented a fuzzy-tuned PID controller, which I have optimized by genetic algorithm and adaptive neuro-fuzzy system.