Model Predictive Control for Autonomous Driving Vehicles

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for still pose challenges, since vehicle speed steering angle are always subject to strict constraints in dynamics. optimal action angular velocity can be obtained from online objective function, dynamic vehicle’s physical limitations, environmental conditions, surrounding obstacles. This paper presents design a nonlinear model predictive controller hard softened constraints. Nonlinear provides higher probability finding actions maintaining system stability. Different parameters simulated analyzed. Results show that with considerably improve ability track exactly on different trajectories.