Stabilizing Quadruped Robot Movement Using Fuzzy Logic Control for Yaw Angle Adjustment in Walking and Troting Gait

Authors

Sukma Nurul Izzah
Universitas Gadjah Mada image/svg+xml
Andi Dharmawan
Universitas Gadjah Mada image/svg+xml
Muhammad Auzan
Universitas Gadjah Mada image/svg+xml
Bakhtiar Alldino Ardi Sumbodo
Universitas Gadjah Mada image/svg+xml
Jazi Eko Istiyanto
Universitas Gadjah Mada image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v12i4.5837
Download PDF
Published: 14-12-2024 • Issue: Vol. 12 No. 4: December 2024 • Section: Regular paper

Abstract

Balance is a fundamental aspect of quadruped robots that determines their movement success. Imbalanced movement can affect the robot's orientation, leading to potential deviations from the intended direction due to changes in the attitude angle. An unstable attitude angle can result in loss of control, complicating effective navigation. This loss of control may prevent the robot from maintaining its stability, increasing the risk of falling. This study designs a control system for a quadruped robot using fuzzy control system to manage the yaw angle while the robot walks forward using both walking and trotting gaits. The fuzzy control system outputs are used to adjust the hip joint angles of the robot's four legs, modifying the stride length of each leg accordingly. The quadruped robot was tested with both walking and trotting gaits moving forward for 30 seconds. The quadruped robot successfully maintained balance and stability in the ð‘§-axis (yaw) on a flat, obstacle-free surface using fuzzy control system. The fuzzy logic control effectively reduced positional distance fluctuations from the set point and enhanced the robot's ability to return to the set point after fluctuations, without producing excessive overshoot.
Keywords: robot; overshoot; fuzzy control; stabilizer; walking robot

References

P. Biswal and P. K. Mohanty, “Development of quadruped walking robots: A review,†Ain Shams Engineering Journal, vol. 12, no. 2, pp. 2017–2031, Jun. 2021, doi: 10.1016/j.asej.2020.11.005.

H. Jiang et al., “Stable skill improvement of quadruped robot based on privileged information and curriculum guidance,†Rob Auton Syst, vol. 170, no. 2022, p. 104550, Dec. 2023, doi: 10.1016/j.robot.2023.104550.

C. Yu, L. Zhou, H. Qian, and Y. Xu, “Posture Correction of Quadruped Robot for Adaptive Slope Walking,†in 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE, Dec. 2018, pp. 1220–1225. doi: 10.1109/ROBIO.2018.8665093.

A. T. B. Antok et al., “Quadruped Robot Balance Control For Stair Climbing Based On Fuzzy Logic,†in 2021 International Electronics Symposium (IES), IEEE, Sep. 2021, pp. 552–557. doi: 10.1109/IES53407.2021.9594046.

U. Keamorakot and L. Poolperm, “Development of the quadruped robot modeling and movement simulation for Education,†in 2021 6th International STEM Education Conference (iSTEM-Ed), IEEE, Nov. 2021, pp. 1–4. doi: 10.1109/iSTEM-Ed52129.2021.9625115.

S. Gu, F. Meng, B. Liu, X. Chen, Z. Yu, and Q. Huang, “Implementing dog-like quadruped robot turning motion based on key movement joints extraction,†Expert Syst Appl, vol. 256, no. June, p. 124887, Dec. 2024, doi: 10.1016/j.eswa.2024.124887.

P. Saraf, A. Sarkar, and A. Javed, “Terrain Adaptive Gait Transitioning for a Quadruped Robot using Model Predictive Control,†in 2021 26th International Conference on Automation and Computing (ICAC), IEEE, Sep. 2021, pp. 1–6. doi: 10.23919/ICAC50006.2021.9594065.

P. Wasilewski and J. Tolstoj-Sienkiewicz, “Modeling and simulation of a parallel quadruped robot,†in 2019 20th International Carpathian Control Conference (ICCC), IEEE, May 2019, pp. 1–5. doi: 10.1109/CarpathianCC.2019.8765958.

G. A. Prasetyo, A. F. I. Suparman, Z. Nasution, E. H. Binugroho, and A. Darmawan, “Development of the Gait Planning for Stability Movement on Quadruped Robot,†IES 2019 - International Electronics Symposium: The Role of Techno-Intelligence in Creating an Open Energy System Towards Energy Democracy, Proceedings, pp. 376–381, 2019, doi: 10.1109/ELECSYM.2019.8901526.

X. Chen et al., “Realization of indoor and outdoor localization and navigation for quadruped robots,†Procedia Comput Sci, vol. 209, pp. 84–92, 2022, doi: 10.1016/j.procs.2022.10.102.

Z. Chen, Q. Xi, C. Qi, X. Chen, Y. Gao, and F. Gao, “Fault-tolerant gait design for quadruped robots with two locked legs using the GF set theory,†Mech Mach Theory, vol. 195, no. October 2023, p. 105592, May 2024, doi: 10.1016/j.mechmachtheory.2024.105592.

J. Zhu, Y. Zhu, and P. Zhang, “Review of Advancements in Wall Climbing Robot Techniques,†Franklin Open, p. 100148, Aug. 2024, doi: 10.1016/j.fraope.2024.100148.

S. Yuan, Y. Zhou, and C. Luo, “Crawling Gait Planning Based on Foot Trajectory Optimization for Quadruped Robot,†in 2019 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE, Aug. 2019, pp. 1490–1495. doi: 10.1109/ICMA.2019.8816477.

J. Chen, K. Xu, and X. Ding, “Adaptive gait planning for quadruped robot based on center of inertia over rough terrain,†Biomimetic Intelligence and Robotics, vol. 2, no. 1, p. 100031, Mar. 2022, doi: 10.1016/j.birob.2021.100031.

A. A. Aldair, A. Al-Mayyahi, and W. Wang, “Design of a Stable an Intelligent Controller for a Quadruped Robot,†Journal of Electrical Engineering & Technology, vol. 15, no. 2, pp. 817–832, Mar. 2020, doi: 10.1007/s42835- 019-00332-5.

K. A. Mishra et al., “Fuzzy logic controlled autonomous quadruped robot,†Mater Today Proc, vol. 63, pp. 49–55, 2022, doi: 10.1016/j.matpr.2022.02.239.

J. Sun, L. Zhou, Y. Li, H. Xu, and B. Geng, “Modeling and hierarchical fuzzy control for locomotion control of the quadruped robot,†2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022, pp. 1408–1413, 2022, doi: 10.1109/ROBIO55434.2022.10011686.

M. Auzan, D. Lelono, and A. Dharmawan, “Humanoid Walking Control Using LQR and ANFIS,†Journal of Robotics and Control (JRC), vol. 4, no. 4, pp. 548–556, Aug. 2023, doi: 10.18196/jrc.v4i4.16444.

N. T. Minh Nguyet and D. X. Ba, “A neural flexible PID controller for task-space control of robotic manipulators,†Front Robot AI, vol. 9, no. January, pp. 1–10, Jan. 2023, doi: 10.3389/frobt.2022.975850.

J.-Y. Kim, H.-M. Kim, S.-K. Kim, J.-H. Jeon, and H.-K. Choi, “Designing an Energy Storage System Fuzzy PID Controller for Microgrid Islanded Operation,†Energies (Basel), vol. 4, no. 9, pp. 1443–1460, Sep. 2011, doi: 10.3390/en4091443.

M. Raković et al., “Fuzzy position-velocity control of underactuated finger of FTN robot hand,†Journal of Intelligent & Fuzzy Systems, vol. 34, no. 4, pp. 2723–2736, Apr. 2018, doi: 10.3233/JIFS-17879.

S. Pati, M. Patnaik, and A. Panda, “Comparative performance analysis of fuzzy PI, PD and PID controllers used in a scalar controlled induction motor drive,†2014 International Conference on Circuits, Power and Computing Technologies, ICCPCT 2014, vol. 72, no. 1, pp. 910–915, Mar. 2014, doi: 10.1109/ICCPCT.2014.7054799.

M. Abdelwahab, V. Parque, A. A. Abouelsoud, and A. M. R. Fath, “Navigation of Omni-Directional Mobile Robot in Unstructured Environments using Fuzzy Logic Control,†in 2021 IEEE/SICE International Symposium on System Integration (SII), IEEE, Jan. 2021, pp. 684–689. doi: 10.1109/IEEECONF49454.2021.9382654.

W. Saeed Majeed, A. Ibrahem Nasser, and K. Rasheed Hameed, “Improve the performance of automatic voltage regulator for power system using self-tuning fuzzy-PID controller,†Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 3, p. 1247, Mar. 2023, doi: 10.11591/ijeecs.v29.i3.pp1247-1257.

Y.-R. Kim, “Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control,†International Journal of Fuzzy Logic and Intelligent Systems, vol. 14, no. 3, pp. 188–199, Sep. 2014, doi: 10.5391/IJFIS.2014.14.3.188.

P. Sarkhel, N. Banerjee, and N. B. Hui, “Fuzzy logic-based tuning of PID controller to control flexible manipulators,†SN Appl Sci, vol. 2, no. 6, p. 1124, Jun. 2020, doi: 10.1007/s42452-020-2877-y.

T. K. Priyambodo and A. Dharmawan, “Auto Vertical Takeoff and Landing on quadrotor using PID-fuzzy,†Journal of Engineering and Applied Sciences, vol. 12, no. Specialissue3, pp. 6420–6425, 2017, doi: 10.3923/jeasci.2017.6420.6425.

T. K. Priyambodo, A. Dharmawan, and A. E. Putra, “PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization,†in AIP Conference Proceedings, 2016, p. 020013. doi: 10.1063/1.4940261.

V. Klemm et al., “Ascento: A Two-Wheeled Jumping Robot,†in 2019 International Conference on Robotics and Automation (ICRA), IEEE, May 2019, pp. 7515–7521. doi: 10.1109/ICRA.2019.8793792.

P. Li, B. Yin, L. Zhang, and Y. Zhao, “Adaptive control algorithm for quadruped robots in unknown high-slope terrain,†Journal of Engineering Research, no. April, May 2024, doi: 10.1016/j.jer.2024.05.018.

T. Joseph, A. Shaikh, M. Sarode, and Y. Srinivasa Rao, “Quadruped Robots: Gait Analysis and Control,†2020 IEEE 17th India Council International Conference, INDICON 2020, 2020, doi: 10.1109/INDICON49873.2020.9342521.

Y. Zhong, R. Wang, H. Feng, and Y. Chen, “Analysis and research of quadruped robot’s legs: A comprehensive review,†Int J Adv Robot Syst, vol. 16, no. 3, p. 172988141984414, May 2019, doi: 10.1177/1729881419844148.

S. Suzuki, T. Kano, A. J. Ijspeert, and A. Ishiguro, “Sprawling Quadruped Robot Driven by Decentralized Control With Cross-Coupled Sensory Feedback Between Legs and Trunk,†Front Neurorobot, vol. 14, no. January, pp. 1–8, Jan. 2021, doi: 10.3389/fnbot.2020.607455.

L. Ye, H. Liu, X. Wang, B. Liang, and B. Yuan, “Multi-task Control for a Quadruped Robot with Changeable Leg Configuration,†in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Oct. 2020, pp. 3944–3950. doi: 10.1109/IROS45743.2020.9340965.

B. Ma, Z. Liu, C. Peng, and X. Li, “Trotting gait control of quadruped robot based on Trajectory Planning,†in 2021 4th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), IEEE, Nov. 2021, pp. 105–108. doi: 10.1109/WCMEIM54377.2021.00031.

Z. Li and Y. Tan, “Trotting Motion of the Quadruped Model with Two Spinal Joints and Its Dynamics Features,†Journal of Robotics, vol. 2020, pp. 1–14, Jun. 2020, doi: 10.1155/2020/3156540.

R. N. Jazar, Theory of Applied Robotics. Boston, MA: Springer US, 2010. doi: 10.1007/978-1-4419-1750-8.

A. Roy Chowdhury, G. S. Soh, S. H. Foong, and K. L. Wood, “Experiments in Second Order Sliding Mode Control of a CPG based Spherical Robot,†IFAC-PapersOnLine, vol. 50, no. 1, pp. 2365–2372, Jul. 2017, doi: 10.1016/J.IFACOL.2017.08.426.

M. M. Kawakibi, A. Dharmawan, M. A. Jazi Eko Istiyanto, and D. Lelono, “Stability Control of Humanoid Robot Walking Speed Using Linear Quadratic Regulator Method,†ICIC Express Letters, Part B: Applications, vol. 15, no. 11, pp. 1107–1115, Nov. 2024, doi: 10.24507/icicelb.15.11.1107.

T. K. Priyambodo, A. Dharmawan, and A. E. Putra, “PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization,†in AIP Conference Proceedings, 2016, p. 020013. doi: 10.1063/1.4940261.

A. Dharmawan, J. E. Istiyanto, A. E. Putra, and M. Auzan, “LQR Compensated by Fuzzy for Kicking Balance Control of a Humanoid Robot,†ICIC Express Letters, Part B: Applications, vol. 14, no. 5, pp. 449–456, May 2023, doi: 10.24507/icicelb.14.05.449.

A. Dharmawan, C. Habiba, and M. Auzan, “Walking stability control system on humanoid when turning based on LQR method,†International Journal of Scientific and Technology Research, vol. 8, no. 11, pp. 2606–2611, 2019.

Author Biographies

Andi Dharmawan
Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada

How to Cite

[1]
S. N. Izzah, A. Dharmawan, M. Auzan, B. A. A. Sumbodo, and J. E. Istiyanto, “Stabilizing Quadruped Robot Movement Using Fuzzy Logic Control for Yaw Angle Adjustment in Walking and Troting Gait”, IJEEI, vol. 12, no. 4, pp. 975–990, Dec. 2024, doi: 10.52549/ijeei.v12i4.5837.