In recent years, autonomous driving technologies have become a topic of growing interest
due to the promise of safer and more convenient mode of transportation. An essential
element in every autonomous driving system is the control algorithm. Classical control
schemes, like PID, are not able to manage Multiple Inputs-Multiple Outputs, complex,
non-linear systems. A more recent control strategy is Model predictive control (MPC),
a modern control method that has shown promising results in systems with complex
dynamics. In MPC, a sequence of optimal control inputs are predicted within a short
time horizon based on the car dynamics, and soft or hard restriction of the system.
In this work, three different nonlinear-MPC (NMPC) controllers were formulated based
on a kinematic, and two dynamic models (double-track and single-track). The steering
system’s dynamics were additionally identified using experimental data. Each MPC
was solved applying direct methods, by transforming the optimal control problem to
a Nonlinear programming (NLP) problem using the Multiple shooting scheme with a
Runge-Kutta 4 integrator. The NLPs were solved using the state-of-the-art optimization
solver IpOpt. Before the real-time implementation, all the NMPC controllers were
simulated in different scenarios and multiple configurations. The results allowed to
select the most suitable controllers to be implemented in a 1:5 scale robotic car.
Finally, two NMPC controllers based on the kinematic, and the single-track dynamic
model were implemented in the robotic car. The algorithms were tested in two different
scenarios at the maximum possible speed. The obtained results from the tests were very
promising, and provide compelling evidence that MPC could be implemented as the
core of future autonomous driving algorithms, since it computes the optimal control
inputs, taking in consideration the restrictions inherent to the system.Tesi
