Transition Control of a Double-Inverted Pendulum System Using Sim2Real Reinforcement Learning

This study presents a sim2real reinforcement learning-based controller for transition control in a double-inverted pendulum system, addressing the limitations of traditional control methods that rely on precomputed trajectories and lack adaptability to strong external disturbances. By introducing the novel concept of ‘transition control’, this research expands the scope of inverted pendulum studies to tackle the challenging task of navigating between multiple equilibrium points. To overcome the reality gap—a persistent challenge in sim2real transfer—a hardware-centered approach was employed, aligning the physical system’s mechanical design with high-fidelity dynamic equations derived from the Euler–Lagrange equation. This design eliminates the need for software-based corrections, ensuring consistent and robust system performance across simulated and real-world environments. Experimental validation demonstrates the controller’s ability to reliably execute all 12 transition scenarios within the double-inverted pendulum system. Additionally, it exhibits recovery characteristics, enabling the system to stabilize and return to equilibrium point even under severe disturbances.