Constrained Model Predictive Control for Low-cost Autonomous Driving With Stability Guarantees

The cost of autonomous drivings system is currently one of the key factors that will decide whether this technology can be widely deployed. In this paper, the control system design of an autonomous vehicle with low-cost mechanical structure and electrical system is studied. The open-loop responses of the velocity and steering system illustrate that this vehicle is confronted with limited hardware performance. The mean goal of this research is applying high performance algorithm to stabilize the system and guarantee the stability as much as possible. In other words, sacrificing some computation to compensate the limited hardware. Model predictive control (MPC) is a control algorithm based on rolling optimization, although it requires more computation than traditional control algorithms it can taking multi complex constraints into account so that it can tackle various problems in this system. A constrained MPC is proposed to control this low-cost vehicle, it is tested on the simulation and real vehicle. By comparing the system performance in open-loop and close-loop, the algorithm is proved to be effective. Finally, the Lyapunov stability proof is also given out in this paper, which guarantees the theoretical stability of the algorithm.