Dynamic Modeling and Control of Underactuated Planar Bipedal Walking

Sovukluk, Sait
This study demonstrates an adaptive model predictive control method for input constrained control of underactuated bipedal walking with a predefined trajectory. Our approach aims to increase the trajectory tracking performance of the system and produce realistic and applicable responses while letting a certain amount of posture change around the predefined trajectory. To do so, we employ whole-body dynamics in our control structure, include weights for the unactuated joint inside the cost function, and define input torque constraints in the solution. Obeying input torque limits decreases modeling and estimation errors, such that trajectory tracking becomes more robust and efficient. Additionally, we test this model-based controller successfully against various disturbances such as high magnitude modeling errors in its weight, significant initial condition errors, pushing and pulling the torso aggressively throughout a step, high percentage input noises, and their combinations. These disturbances are usually introduced in the torso because it is the heaviest, the longest, and the unactuated joint. Thanks to its short-horizon requirement, the controller is suitable for implementation in real-time and in 1kHz frequency, which is usually required to control high-dimensional underactuated nonlinear hybrid systems.


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Citation Formats
S. Sovukluk, “Dynamic Modeling and Control of Underactuated Planar Bipedal Walking,” M.S. - Master of Science, Middle East Technical University, 2022.