Race Car Control Optimization
Enhanced race car performance through state-space analysis.
In the month-long project titled “Race Car Control Optimization and LQR Integration,” spanning from October 22 to November 22, our objective was to comprehensively explore control strategies to enhance the performance of a race car in a simulation environment.
I initiated my approach with an in-depth exploration of state-space analysis and advanced control theory. Through meticulous fine-tuning of control parameters based on state-space modeling, I aimed to achieve optimal control of the race car, considering crucial dynamics such as position, velocity, and other relevant state variables.
A notable accomplishment was the successful integration of an LQR (Linear Quadratic Regulator) controller into the simulation. The LQR controller, renowned for its effectiveness in optimizing control systems, proved transformative for our race car. This integration resulted in a significant performance boost, notably reducing track traversal time by an impressive 2x compared to the initial PID (Proportional-Integral-Derivative) controller setup. Despite this remarkable speed improvement, our new control system maintained a mean deviation of just 0.48 meters, showcasing precision and stability.
This achievement not only highlights the power of advanced control techniques in the realm of race car control but also has the potential to revolutionize motorsport by significantly enhancing both speed and safety in competitive racing events.