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Learning Control Applications for Autonomous Driving in Extreme Maneuver Scenarios
Tong Duy Son,Lanh Nguyen,Herman Van der Auweraer 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
This work presents the applications of iterative learning control for autonomous vehicle in extreme maneuver scenarios. By exploiting data from previous executions, the proposed learning algorithm that uses a simple kinematic model can generate optimal feedforward control signals to improve vehicle control performance against nonlinearity, model uncertainties and disturbance. The design developments are validated using a co-simulation structure of Siemens Simcenter Amesim and Prescan software. Simcenter Amesim provides an integrated simulation platform to predict the performance of vehicle dynamics, while Simcenter Prescan is a physics-based platform that can simulate traffic scenarios and sensors. We validate and analyze the results with two driving use cases: racing car and drift parking.