Group that realized the project:
- Bertamè Sebastiano - [email protected]
- Rapallini Antonio - [email protected]
This project consist in the design and implementation of an optimal control law for a vehicle described by simple bicycle model.
The work is structured into a series of tasks, each building upon the last to evolve our understanding and control of the vehicle model. We start by discretizing the dynamics and proceed to generate optimal trajectories, ensuring the stability and efficiency of vehicle motion. The subsequent tasks involve tracking the trajectory through Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) strategies, culminating in a visualization of the vehicle executing the tracking of the optimal trajectory evaluated with the LQR.
The job has been done entirely in Python, and this report documents our approach, our analyses, and implemented solutions for addressing the challenges posed by optimal vehicle control. We present our findings, supported by graphical representations for clarity and visualization.