Model Complexity and Coupling of Longitudinal and Lateral Control in Autonomous Vehicles Using Model Predictive Control
نویسنده
چکیده
Autonomous vehicles and research pertaining to them have been an important topic in academia and industry in recent years. Developing controllers that enable vehicles to perform path and trajectory following is a diverse topic where many different control strategies are available. In this thesis, we focus on lateral and longitudinal control of autonomous vehicles and two different control strategies are considered: a standard decoupled control and a new suggested coupled control. In the decoupled control, the lateral controller consists of a linear time-varying model predictive controller (LTV-MPC) together with a PI-controller for the longitudinal control. The coupled controller is a more complex LTV-MPC which handles both lateral and longitudinal control. The objective is to develop both control strategies and evaluate their design and performance through path following simulations in a MATLAB environment. When designing the LTV-MPC, two vehicle models are considered: a kinematic model without tyre dynamics and a dynamic bicycle model with tyre forces derived from a linear Pacejka model. A research on how model complexity affects tracking performance and solver times is also performed. In the end, the thesis presents the findings of the different control strategies and evaluate them in terms of tracking performance, solver time, and ease of implementation.
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