Dimension reduction of inverse kinematics of seven-degree-of-freedom manipulator based on pose separation method

Zhang, Xinqing; You, Dazhang; Zhou, Yang; Zeng, Yu; Ai, Xi; Wang, Shuai

doi:10.1109/wcmeim54377.2021.00008

Cited by 1 publication

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“…Humanoid robots are known for their high mobility and adaptability to diverse environments, driven by their complex degree of freedom (DOF) and dynamics [1]- [3]. While this abundance of DOF endows humanoid robots with the ability to perform various tasks [4]- [7], it also leads to challenges in controlling their intricate body design, power distribution, motion patterns, and movement algorithms [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Humanoid Walking Control Using LQR and ANFIS

Auzan,

Lelono,

Dharmawan

2023

Journal of Robotics and Control

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Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.

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