Attitude and Horizontal Speed Control System on Unmanned Aerial Vehicle Using LQR

Dharmawan, Andi; Ichsan, Lazuardi; Baskoro, Hanif; Istiyanto, Jazi Eko; Handayani, Ariesta Martiningtyas

doi:10.1109/icst47872.2019.9166192

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The value is still within the support polygon area, where the area margin is 0.039 meters. The result is also in line with [61] research, where even though there is an overshoot, the humanoid robots can still perform static walking without falling. But, our hypothesis, if external force occurs, such as a leg swing when walking, this overshoot will increase and make a robot fall.…”

Section: Resultssupporting

confidence: 89%

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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Section: Resultssupporting

confidence: 89%