Comparative Vehicle Stability Analysis of Indoor RC Vehicle Using Control Techniques

Autonomous and robotic vehicle systems depend heavily on vehicle stability control to maintain performance, safety, and maneuverability under changing circumstances. Three popular control methods—Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC)—applied to an indoor robotic vehicle are compared in this work in order to compare stability control. Key vehicle factors such as mass, yaw inertia, centre of gravity (CG) distribution, and front and rear tire cornering stiffness are taken into account in the research to assess their impact on vehicle dynamics. Yaw rate, slip angle, steering angle, and velocity are all controlled by each control method, and tracking accuracy, response time, and control effort are used to evaluate performance. MPC uses predictive optimization for improved performance, LQR maximizes stability through state feedback, and PID control offers a straightforward yet efficient approach. Graphical depictions and simulation results show the advantages and disadvantages of each strategy for preserving vehicle stability. The results aid in the creation of effective control schemes for self-driving robotic cars, allowing for increased stability and versatility in practical applications.