Model predictive control (MPC) using randomized optimization is expected to solve different control problems. However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the autonomous driving using a radio-controlled (RC) car. First of all, a sample generation technique in the frequency domain is discussed. This prevents undesirable randomness which affect the smoothness of the steering operation. Second, the proposed randomized MPC is implemented on a Graphics Processing Unit (GPU). The expected GPU acceleration in calculation speed at various problem sizes is also presented. The results show the improved control performance and computational speed that was not achievable using CPU based implementation. Besides, the selection of parameters for randomized MPC is discussed. The usefulness of the proposed scheme is demonstrated by both simulation and experiments. In the experiments, a 1/10 model RC car is used for collision avoidance task by autonomous driving.
Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving
IEEE Transactions on Intelligent Vehicles ; 7 , 1 ; 11-20
01.03.2022
2679004 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Real-Time Model Predictive Safety Assessment for Level 3 Autonomous Driving
Springer Verlag | 2022
|Model Predictive Control for Safe Autonomous Driving Applications
Springer Verlag | 2022
|Towards Real-Time Model Predictive Control Approach for Autonomous Active Steering
British Library Conference Proceedings | 2006
|Predictive control system for autonomous driving vehicle
Europäisches Patentamt | 2018
|