Adaptive MPC-based quadrupedal robot control under periodic disturbances
This work addresses a specific case of disturbances for quadrupedal robots, but it is incremental as it builds on existing adaptive control methods.
The paper tackled the problem of periodic disturbances in quadrupedal robot locomotion by developing a lightweight regressor to estimate disturbance magnitude and frequency, resulting in improved performance over baseline static compensation.
Recent advancements in adaptive control for reference trajectory tracking enable quadrupedal robots to perform locomotion tasks under challenging conditions. There are methods enabling the estimation of the external disturbances in terms of forces and torques. However, a specific case of disturbances that are periodic was not explicitly tackled in application to quadrupeds. This work is devoted to the estimation of the periodic disturbances with a lightweight regressor using simplified robot dynamics and extracting the disturbance properties in terms of the magnitude and frequency. Experimental evidence suggests performance improvement over the baseline static disturbance compensation. All source files, including simulation setups, code, and calculation scripts, are available on GitHub at https://github.com/aidagroup/quad-periodic-mpc.