Adaptive Cruise Predictive Control Based on Particle Swarm Optimization

To further improve the predictive control accuracy of multi-objective adaptive cruise system, an adaptive cruise predictive control algorithm based on particle swarm optimization was proposed. Firstly, a longitudinal kinematics model with front vehicle acceleration disturbance of adaptive cruise system was established and linearly discretized. Then, synthesizing the distance error, relative speed, acceleration and impact, a quadratic multi-objective optimization performance index function and multi-parameter constraints were designed, and an adaptive cruise predictive control optimization problem was constructed. Finally, in order to solve the problem easily, the objective function and constraints were deduced into a normative form with predictive control increment as the optimization variable, and the optimal control law of adaptive cruise predictive control was solved based on particle swarm optimization algorithm. The simulation results of Maltab/Simulink under multiple working conditions show that the optimal control law solved by particle swarm optimization algorithm can control the self-driving vehicle to maintain better tracking and self-adaptability.