Filtered Regression Estimation-Based Signal Compensation for Swinging Up Rotary Inverted Pendulum: Theoretical Results and Experimental Validations

In this paper, we study the swing-up control for the rotary inverted pendulum (RIP) system with its actuated joint subject to uncertainty. We adapt a signal compensation method which divides the system’s uncertainty into a previously sampled unknown term and its variation. We validate our results via theoretical analysis and experimental investigations.

Firstly, we improve the signal compensation method by designing a filtered regression estimation model for the previously sampled unknown term to avoid directly measuring or calculating the angular acceleration. Secondly, we prove the convergence of the state variables of the closed-loop system with uncertainty under the improved signal compensation-based swing-up controllers.

We present simulation and experimental results which demonstrate the improved signal compensation method’s ability to efficiently tackle the uncertainty, leading reduced instantaneous and average control input, decreased arm oscillation, and shortened swing-up time.

Our work offers promising insights into compensating uncertainty at the actuated joints of underactuated systems. This could potentially enhance control performance and extend the service life of equipment.