🤖 AI Summary
This work proposes an efficient real-time nonlinear model predictive control (NMPC) approach for the remote underactuated double pendulum system under unknown parameters and limited interaction time. By integrating a structure-exploiting alternating direction method of multipliers (ADMM) with an interior-point-accelerated sequential quadratic programming (SQP) solver, the method achieves global swing-up and stabilization without requiring prior model knowledge. The proposed framework is directly deployed on the CloudPendulum remote hardware platform, satisfying stringent real-time constraints while demonstrating strong robustness and disturbance rejection capabilities. Experimental results confirm its effectiveness in reliably achieving both swing-up and stable regulation of the double pendulum system.
📝 Abstract
The 4th "AI Olympics with RealAIGym" competition, to be held at IJCAI-ECAI 2026 in Bremen, challenges participants to develop a global control policy for swinging up and stabilizing an underactuated two-link system in its upright position. In contrast to previous editions, participants develop and evaluate their control strategies directly on remotely accessible CloudPendulum hardware, with limited interaction time and without prior knowledge of the system's model parameters. This paper presents an optimal-control-based approach employing real-time nonlinear model predictive control implemented using sequential quadratic programming. The results demonstrate that the proposed SQP-based MPC controller achieves reliable swing-up and stabilization performance, while maintaining robustness against disturbances.