Controlling Center of Mass in Humanoid Robot using Sliding Mode Control

Nguyen, Xuan Tien; Tran, Tri Duc; Nguyen, Huy Hung; Tong, Nhut Phuong; Nguyễn, Thanh Phương; Nguyen, Tan Tien

doi:10.1109/icamechs49982.2020.9310088

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Despite advances in generating walking patterns based on COM or ZMP criteria [23], [24], the complex nature of humanoid robots can lead to discrepancies between generated patterns and actual responses. Control systems are crucial for accurate trajectory tracking and overcoming discrepancies [25]- [27], with deterministic methods like Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) commonly employed [28]- [30]. These methods have encouraged various studies that have succeeded in realizing stable humanoid robots with COM and ZMP criteria as the popular criteria [31]- [33].…”

Section: Introductionmentioning

confidence: 99%

“…The control system maintains the robot states, for example, the COM and ZMP position, based on the states' error calculated from the system feedback [41]- [43]. Various control systems that have been widely introduced include Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), a linear control system [28]- [30]. PID is a control system that has been long and robust track record.…”

Section: Introductionmentioning

confidence: 99%

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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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