When: 5th of February, 1:00pm AEDT
Where: This seminar will be partially presented at the ACFR seminar area, J04 lvl 2 (Rose St Building) and partially online via Zoom. RSVP
Speaker: Jaehyun Lim
Title: ADMM-based Time-Optimal Nonlinear Model Predictive Control for Autonomous Vehicle Racing
Abstract:
In the past decade, many studies have proposed fast nonlinear model predictive control (NMPC) methods for autonomous racing based on sequential quadratic programming (SQP) and the real-time iteration (RTI) scheme, but they still have limitations in terms of trade-offs among numerical stability, performance, and execution speed. To address this limitation, we define a time-optimal NMPC problem with a separable objective function, thereby relaxing the time-optimization problem into a relatively easy-to-solve problem by separating it from vehicle dynamics and inequality constraints, and then propose a solution using the alternating direction multiplier method (ADMM). The resulting ADMM-SQP algorithm is embedded into an RTI scheme (ADMM-RTI), which performs a single SQP/ADMM step per control interval. Lap-time trial experiments demonstrate that ADMM-RTI achieves superior lap times and robust feasibility compared to several representative NMPC-based racing formulations, while maintaining median RTI solve times around 3.6-4.0 ms and worst-case times below 12 ms. Additional multi-vehicle simulations confirm that the signed-distance-field-based collision-avoidance formulation can be integrated into the proposed ADMM-RTI controller to enable safe overtaking maneuvers.
Bio:
Jaehyun Lim received the B.S. degree in mechanical engineering, in 2017, from Yonsei University, Seoul, South Korea. He is currently working toward his Ph.D. degree in the field of machine learning and control systems at Yonsei University. His research interests include machine learning, optimal control, robotics, and self-driving vehicles.
