Design an Intelligent Balanced Control of Quadruped Legs Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sigit Wasista (1), Handayani Tjandrasa (2), Supeno Djanali (3)
(1) Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
(2) Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
(3) Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Wasista, Sigit, et al. “Design an Intelligent Balanced Control of Quadruped Legs Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3, June 2023, pp. 901-10, doi:10.18517/ijaseit.13.3.18589.
This study discusses the 12 DoF stability control algorithm for Quadruped robot legs to adjust the balance on irregular terrain. The source of the instability is the irregularity of the ground surface and external forces. Therefore, dynamic stability criteria are needed to plan the robot's movement and restore balance for the movement of a four-legged robot with a dynamic gait over an irregular terrain. The novelty of this study is the use of 12 ANFIS at once to manage the 12 DoF of each leg, which are grouped into four sections, and each section consists of 3 ANFIS. The ANFIS method is used as an algorithm to move the 12-DoF robot legs by training some robot leg movement data based on the slope angle of the surface. The results of training with the ANFIS method can be optimal if the number of rules is close to the given training data. From 29 body tilt angle position data and 12-DOF robot legs, good results will be obtained if the 5x5 number membership function is used for each input which will produce 25 ANFIS rules and combined using the Gaussian type so that it can produce RMSE = 0.068233. The next research is to develop reliable methods such as Zero Moment Point (ZMP) combined with the BPNN or ANFIS methods so that it is expected to get a reliable robot body balance.

J. Kim, T. Kang, D. Song and S.-J. Yi, "PAWDQ: A 3D Printed, Open Source, Low Cost Dynamic Quadruped," in 18th International Conference on Ubiquitous Robots (UR), Gangneung-si, Gangwon-do, Korea, July 12-14, 2021.

Y. Shi, S. Li, M. Guo, Y. Yang, D. Xia and X. Luo, "Structural Design, Simulation and Experiment of Quadruped Robot," Applied Sciences, vol. 11, no. 22, p. 10705, 2021.

H. Sun, T. Fu, Y. Ling and C. He, "Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning," Sensors, vol. 21, no. 17, p. 5907, 2021.

M. A. Şen, B. Veli and K. Mete, "Three Degree Of Freedom Leg Design For Quadruped Robots And Fractional Order Pid (Piλdµ) Based Control," Konya Journal of Engineering Sciences, vol. 8, no. 2, pp. 237-247,, 2020.

S. Wasista, H. Tjandrasa and W. Wibisono, "Swing Trajectory Model for the New Design of Quadruped Robot Using V-REP Simulator," in International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, 2020.

Y. H. Lee, Y. H. Lee, H. Lee, H. Kang, L. T. Phan, S. Jin, Y. B. Kim, D.-Y. Seok, S. Y. Lee, H. Moon, J. C. Koo and a. H. R. Choi, "Force-controllable Quadruped Robot System with Capacitive-type Joint Torque Sensor," in International Conference on Robotics and Automation (ICRA), Palais des congres de Montreal, Montreal, Canada, May 20-24, 2019.

Y. Jia, X. Luo, B. Han, G. Liang, J. Zhao and Y. Zhao, "Stability Criterion for Dynamic Gaits of Quadruped Robot," Applied Sciences — Open Access Journal, vol. 8, no. 12, November 2018.

J.-S. R. Jang, "ANFIS : Adaptive-Network-Based Fuzzy Inference System," IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, vol. 23, no. 3, pp. 665-685, 1993.

J.-H. Qin, J. Luo, K.-C. Chuang and T.-S. Lan, "Stable Balance Adjustment Structure of the Quadruped Robot Based on the Bionic Lateral Swing Posture," Mathematical Problems in Engineering, vol. 2020, 2020.

J. Cui, Z. Li, Y. Kuang and H. Cheng, "Standing balance maintenance by virtual suspension model control for legged robot," Advances in Mechanical Engineering Robotics: Intelligence, Learning, and Control - Research Article, vol. 12, no. 9, pp. 1-13, 2020.

V. Bakırcıoğlu, M. A. Şen and M. Kalyoncu, "Adaptive Neural-Network Based Fuzzy Logic (ANFIS) Based Trajectory Controller Design for One Leg of a Quadruped Robot," 2016.

X. Zhu, J. Wan, C. Zhou and W. Xu, "A composite robust reactive control strategy for quadruped robot under external push disturbance," Computers and Electrical Engineering, vol. 91, no. : 107027, pp. 1-17, 2021.

T. Sun, Z. Dai and P. Manoonpong, "Distributed-force-feedback-based reflex with online learning for adaptive quadruped motor control," Neural Networks 142 , p. 410-427, 2021.

I. Gonzalez-Luchena, A. Gonzalez-Rodriguez, A. Gonzalez-Rodriguez, C. Adame-Sanchez and F. Castillo-Garcia, "A new algorithm to maintain lateral stabilization during the running gait of a quadruped robot," Robotics and Autonomous Systems 83 , p. 57-72, 2016.

P. Biswal, K. Mohanty and Prases, "Development of quadruped walking robots: A review," Ain Shams Engineering Journal 12, p. 2017-2031, 2021.

S. Tripathy and S. Gaur, "Rough terrain quadruped robot- BigDog," Materials Today: Proceedings, 2021.

C. Gonzalez, V. Barasuol, M. Frigerio, R. Featherstone, D. G. Caldwell and C. Semini, "Line Walking and Balancing for Legged Robots with Point Feet," in RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA (Virtual), October 2020.

J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, J. Lee, V. Tsounis, V. Koltun and M. Hutter, "Learning Agile and Dynamic Motor Skills for Legged Robots," Science Robotics, vol. 4, no. 26, pp. 1-20, 2019.

W. Sun, X. Tian, Y. Song, B. Pang, X. Yuan and Q. Xu, "Balance Control of a Quadruped Robot Based on Foot Fall Adjustment," Applied Sciences , vol. 12, no. 5, pp. 1-14, 2521, February 2022.

M. W. Spong, S. Hutchinson and M. Vidyasagar, Robot Dynamics and control. 1st ed., New York: JOHN WILEY & SONS, INC., 2006.

M. Ning, J. Yang, Z. Zhang, J. Li, Z. Wang, L. Wei and P. Feng, "Method of Changing Running Direction of Cheetah-Inspired Quadruped Robot," Sensors, vol. 24, no. 22, 9601, 2022.

X. Zheng, Y. Zheng, Y. Shuai, J. Yang, S. Yang and Y. Tian, "Kinematics analysis and trajectory planning of 6-DOF robot," in Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2019), Chengdu, China, 2019.

M. Chen, H. Chen, X. Wang, J. Yu and Y. Zhang, "Design and Control of a Novel Single Leg Structure of Electrically Driven Quadruped Robot," Mathematical Problems in Engineering, vol. 2020, no. 21, pp. 1-12, May 2020.

S. Kucuk and Z. Bingul, "Robot Kinematics: Forward and Inverse Kinematics," Industrial-Robotics-Theory-Modelling-Control, vol. 285, no. 8, p. 964, December 2006.

Setiawardhana, R. Dikairono, D. Purwanto and T. Arief Sardjono, "Ball Position Estimation in Goal Keeper Robots Using Neural Network," International Review of Automatic Control (IREACO), vol. 12, no. 1, pp. 38-47, January 2020.

R. Gao, "Inverse kinematics solution of Robotics based on neural network algorithms," Journal of Ambient Intelligence and Humanized Computing , March 2020.

A. Alazzam and T. Tashtoush, "Lead-Free Solder Reliability Modeling Using Adaptive Neuro-Fuzzy Inference System (ANFIS)," Jordan Journal of Mechanical and Industrial Engineering, vol. 15, no. 2, pp. 181-189, 2021.

T. Ibarra-Pí©rez, J. Manuel Ortiz-Rodrí­guez, F. Olivera-Domingo, H. A. Guerrero-Osuna, H. Gamboa-Rosales and M. d. R. Martí­nez-Blanco, "A Novel Inverse Kinematic Solution of a Six-DOF Robot Using Neural Networks Based on the Taguchi Optimization Technique," Applied Sciences, vol. 12, no. 19, pp. 20, 9512, Sep 2022.

S. Wasista, H. Tjandrasa and W. Wibisono, "Quadruped Robot Control Base on Adaptive Neuro-Fuzzy Inference System With V-REP Simulator," in 3rd International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, 2020.

D. Karaboga and E. Kaya, "Adaptive Network Based Fuzzy Inference System (ANFIS) Training Approaches: A Comprehensive Survey," Artificial Intelligence Review, An International Science and Engineering Journal, vol. 52, no. 4, pp. 2263-2293, Dec. 01, 2019.

B. Sarwar, I. S. Bajwa, N. Jamil, S. Ramzan and N. Sarwar, "An Intelligent Fire Warning Application Using IoT and an Adaptive Neuro-Fuzzy Inference System," Sensors, vol. 19, no. 14, pp. 1-18, 3150, July 2019.

H. Khalili, "Path planning of a quadruped robot on a flat surface using ZMP for stability," Journal of Research in Science, Engineering and Technology, vol. 7, no. 2, pp. 6-20, February 2019.

Y. d. Viragh, M. Bjelonic, C. D. Bellicoso, F. Jenelten and M. Hutter, "Trajectory Optimization for Wheeled-Legged Quadrupedal Robots Using Linearized ZMP Constraints," IEEE Robotics And Automation Letters, vol. 4, no. 2, pp. 1633-1640, April 2019.

X. Zhu, M. Wang, X. Ruan, L. Chen, T. Ji and X. Liu, "Adaptive Motion Skill Learning of Quadruped Robot on Slopes Based on Augmented Random Search Algorithm," Electronics, vol. 11, no. 6, 842, pp. 1-15, 2022.

X. Meng, W. Liu, L. Tang, Z. Lu, H. Lin and J. Fang, "Trot Gait Stability Control of Small Quadruped Robot Based on MPC and ZMP Methods," Processes , vol. 11 , no. 1, pp. 1-14, 252, January 2023.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).