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Stewart-Inspired Vibration Isolation Control for a Wheel-legged Robot via Variable Target Force Impedance Control

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Abstract

The vibration isolation control for wheel-legged robot has been widely investigated when adapting to the undulating slope terrain. How to solve the lag problem of low accuracy of foot-end force convergence to fixed target force in traditional impedance control under continuously changing slope terrain is the main challenge. In this paper, a vibration isolation control strategy based on variable target force impedance control (VTFIC) is proposed to effectively realize the foot-end contact force to track the target force under uneven road while maintaining the stability of the body. The strategy includes foot-end disturbance force estimator (FDFE) and force convergence accelerating controller (FCAC). Firstly, FDFE includes slope angle model, slope terrain model, autoregressive comprehensive moving average (ARIMA) model and event-triggering mechanism. It is mainly used to predict and calculate the disturbance force of slope terrain, and solve the problem of high deviation between foot-end actual force and target force caused by the impulse when foot contact with slope. Secondly, FCAC is designed based on power functional feed-forward control, to accelerate the convergence speed of the foot-end contact force to the target force. Finally, the simulation and experiment results show that the foot-end contact force of the robot can effectively track the target force with high accuracy and the robot remains stable under various terrains.

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Data Availability

The data that support the findings of this study are available from the corresponding author Prof. Zhihua Chen upon reasonable request.

References

  1. Chen, Z., Li, J., Wang, J., Wang, S., Zhao, J., Li, J.: Towards hybrid gait obstacle avoidance for a six wheel-legged robot with payload transportation. J. Intell. Robot. Syst. 102(3), 1–21 (2021)

    Article  Google Scholar 

  2. Chen, Z., Li, J., Wang, S., Wang, J., Ma, L.: Flexible gait transition for six wheel-legged robot with unstructured terrains. Robotics and Autonomous Systems p. 103989 (2022)

  3. Huang, D., Yang, C., Pan, Y., Cheng, L.: Composite learning enhanced neural control for robot manipulator with output error constraints. IEEE Trans. Industr. Inf. 17(1), 209–218 (2021)

    Article  Google Scholar 

  4. Li, J., Dai, Y., Wang, J., Su, X., Ma, R.: Towards broad learning networks on unmanned mobile robot for semantic segmentation. In: 2022 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–7. IEEE (2022)

  5. Li, J., Wang, J., Peng, H., Hu, Y., Su, H.: Fuzzy-torque approximation-enhanced sliding mode control for lateral stability of mobile robot. IEEE Trans. Syst. Man Cybern. A Syst. 52(4), 2491–2500 (2022)

    Article  Google Scholar 

  6. Li, J., Wang, J., Peng, H., Zhang, L., Hu, Y., Su, H.: Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction. Neurocomputing 410, 342–353 (2020)

    Article  Google Scholar 

  7. Li, J., Wang, J., Wang, S., Yang, C.: Human-robot skill transmission for mobile robot via learning by demonstration. Neural Computing and Applications pp. 1–11 (2021). https://doi.org/10.1007/s00521-021-06449-x

  8. Li, J., Zhang, X., Li, J., Liu, Y., Wang, J.: Building and optimization of 3d semantic map based on lidar and camera fusion. Neurocomputing 409, 394–407 (2020)

    Article  Google Scholar 

  9. Sartori, D., Quagliotti, F., Rutherford, M.J., Valavanis, K.P.: Implementation and testing of a backstepping controller autopilot for fixed-wing uavs. J. Intell. Robot. Syst. 76(3), 505–525 (2014)

    Article  Google Scholar 

  10. Wang, S., Chen, Z., Li, J., Wang, J., Li, J., Zhao, J.: Flexible motion framework of the six wheel-legged robot: experimental results. IEEE/ASME Transactions on Mechatronics (2021)

  11. Yang, C., Huang, D., He, W., Cheng, L.: Neural control of robot manipulators with trajectory tracking constraints and input saturation. IEEE Trans. Neural Netw. Learn. Syst. 32(9), 4231–4242 (2021)

    Article  MathSciNet  Google Scholar 

  12. Zeng, C., Chen, X., Wang, N., Yang, C.: Learning compliant robotic movements based on biomimetic motor adaptation. Robotics and Autonomous Systems 135, 103,668 (2021)

  13. Endo, G., Hirose, S.: Study on roller-walker–multi-mode steering control and self-contained locomotion. J. Robot. Soc. Japan 18(8), 1159–1165 (2000)

    Article  Google Scholar 

  14. Kim, Y.S., Jung, G.P., Kim, H., Cho, K.J., Chu, C.N.: Wheel transformer: A wheel-leg hybrid robot with passive transformable wheels. IEEE Trans. Robot. 30(6), 1487–1498 (2014)

    Article  Google Scholar 

  15. Tadakuma, K., Tadakuma, R., Maruyama, A., Rohmer, E., Nagatani, K., Yoshida, K., Ming, A., Shimojo, M., Higashimori, M., Kaneko, M.: Mechanical design of the wheel-leg hybrid mobile robot to realize a large wheel diameter. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3358–3365. IEEE (2010)

  16. Grand, C., Benamar, F., Plumet, F.: Motion kinematics analysis of wheeled-legged rover over 3d surface with posture adaptation. Mech. Mach. Theory 45(3), 477–495 (2010)

    Article  MATH  Google Scholar 

  17. Li, X., Zhou, H., Feng, H., Zhang, S., Fu, Y.: Design and experiments of a novel hydraulic wheel-legged robot (wlr). In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3292–3297. IEEE (2018)

  18. Chen, Z., Wang, S., Wang, J., Xu, K., Lei, T., Zhang, H., Wang, X., Liu, D., Si, J.: Control strategy of stable walking for a hexapod wheel-legged robot. ISA Trans. 108, 367–380 (2021)

    Article  Google Scholar 

  19. Xue, J., Wang, S., Li, J., Wang, J., Zhang, J., Chen, Z.: Impedance-based foot-end torque vibration isolation control of parallel structure wheel-leg robot. The 2022 WRC Symposium on Advanced Robotics and Automation (WRC SARA) (2022)

  20. Du, H., Gao, F.: Fault tolerance properties and motion planning of a six-legged robot with multiple faults. Robotica 35(6), 1397–1414 (2017)

    Article  Google Scholar 

  21. Jung, S., Hsia, T., Bonitz, R.: Force tracking impedance control of robot manipulators under unknown environment. IEEE Trans. Control Syst. Technol. 12(3), 474–483 (2004). https://doi.org/10.1109/TCST.2004.824320

    Article  Google Scholar 

  22. Kim, T., Kim, H.S., Kim, J.: Position-based impedance control for force tracking of a wall-cleaning unit. Int. J. Precis. Eng. Manuf. 17(3), 323–329 (2016)

    Article  Google Scholar 

  23. Kronander, K., Billard, A.: Stability considerations for variable impedance control. IEEE Trans. Robot. 32(5), 1298–1305 (2016)

    Article  Google Scholar 

  24. Chen, Z., Wang, S., Wang, J., Xu, K.: Attitude stability control for multi-agent six wheel-legged robot. IFAC-PapersOnLine 53(2), 9636–9641 (2020)

    Article  Google Scholar 

  25. Peng, H., Wang, J., Shen, W., Shi, D.: Cooperative attitude control for a wheel-legged robot. Peer-to-Peer Netw. Appl. 12(6), 1741–1752 (2019)

    Article  Google Scholar 

  26. Xin, Y., Chai, H., Li, Y., Rong, X., Li, B., Li, Y.: Speed and acceleration control for a two wheel-leg robot based on distributed dynamic model and whole-body control. IEEE Access 7, 180,630–180,639 (2019)

  27. Xu, K., Wang, S., Wang, X., Wang, J., Chen, Z., Liu, D.: High-flexibility locomotion and whole-torso control for a wheel-legged robot on challenging terrain*. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 10,372–10,377 (2020)

  28. Yue, B., Wang, S., Chen, Z., Xu, K., Wang, J.: Research on vibration isolation control of six wheel-legged robot based on impedance control. In: 2020 39th Chinese Control Conference (CCC), pp. 3978–3983. IEEE (2020)

  29. Huang, H., He, W., Wang, J., Zhang, L., Fu, Q.: An all servo-driven bird-like flapping-wing aerial robot capable of autonomous flight. IEEE/ASME Transactions on Mechatronics (2022)

  30. Yu, X., He, W., Li, Q., Li, Y., Li, B.: Human-robot co-carrying using visual and force sensing. IEEE Trans. Ind. Electron. 68(9), 8657–8666 (2020)

    Article  Google Scholar 

  31. Yu, X., Li, B., He, W., Feng, Y., Cheng, L., Silvestre, C.: Adaptive-constrained impedance control for human-robot co-transportation. IEEE transactions on cybernetics (2021)

  32. Sharifi, M., Behzadipour, S., Vossoughi, G.: Nonlinear model reference adaptive impedance control for human-robot interactions. Control. Eng. Pract. 32, 9–27 (2014)

    Article  Google Scholar 

  33. Duan, J., Gan, Y., Chen, M., Dai, X.: Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot. Auton. Syst. 102, 54–65 (2018)

    Article  Google Scholar 

  34. Xu, K., Wang, S., Yue, B., Wang, J., Peng, H., Liu, D., Chen, Z., Shi, M.: Adaptive impedance control with variable target stiffness for wheel-legged robot on complex unknown terrain. Mechatronics 69, 102,388 (2020)

  35. Kasaei, M., Abreu, M., Lau, N., Pereira, A., Reis, L.P.: Robust biped locomotion using deep reinforcement learning on top of an analytical control approach. Robotics and Autonomous Systems 146, 103,900 (2021)

  36. Vincent, I., Sun, Q.: A combined reactive and reinforcement learning controller for an autonomous tracked vehicle. Robot. Auton. Syst. 60(4), 599–608 (2012)

    Article  Google Scholar 

  37. Li, Z., Liu, J., Huang, Z., Peng, Y., Pu, H., Ding, L.: Adaptive impedance control of human-robot cooperation using reinforcement learning. IEEE Trans. Ind. Electron. 64(10), 8013–8022 (2017). https://doi.org/10.1109/TIE.2017.2694391

    Article  Google Scholar 

  38. Izadbakhsh, A., Khorashadizadeh, S., Ghandali, S.: Robust adaptive impedance control of robot manipulators using szász–mirakyan operator as universal approximator. ISA Transactions 106, 1–11 (2020). https://doi.org/10.1016/j.isatra.2020.06.017.https://www.sciencedirect.com/science/article/pii/S0019057820302640

  39. Brahmi, B., Driscoll, M., El Bojairami, I.K., Saad, M., Brahmi, A.: Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer. ISA Transactions 108, 381–392 (2021). https://doi.org/10.1016/j.isatra.2020.08.036.https://www.sciencedirect.com/science/article/pii/S0019057820303682

  40. Roveda, L., Maskani, J., Franceschi, P., Abdi, A., Braghin, F., MolinariTosatti, L., Pedrocchi, N.: Model-based reinforcement learning variable impedance control for human-robot collaboration. Journal of Intelligent & Robotic Systems (2020)

  41. Peng, H., Wang, J., Wang, S., Shen, W., Shi, D., Liu, D.: Coordinated motion control for a wheel-leg robot with speed consensus strategy. IEEE/ASME Trans. Mechatron. 25(3), 1366–1376 (2020)

    Google Scholar 

  42. Zhihua, C., Shoukun, W., Kang, X., Junzheng, W., Jiangbo, Z., Shanshuai, N.: Research on high precision control of joint position servo system for hydraulic quadruped robot. In: 2019 Chinese Control Conference (CCC), pp. 755–760. IEEE (2019)

  43. Hao, R., Wang, J., Zhao, J., Wang, S.: Observer-based robust control of 6-dof parallel electrical manipulator with fast friction estimation. IEEE Trans. Autom. Sci. Eng. 13(3), 1399–1408 (2016). https://doi.org/10.1109/TASE.2015.2427743

    Article  Google Scholar 

  44. Shi, D., Xue, J., Zhao, L., Wang, J., Huang, Y.: Event-triggered active disturbance rejection control of dc torque motors. IEEE/ASME Trans. Mechatron. 22(5), 2277–2287 (2017)

    Article  Google Scholar 

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Funding

This work was supported by the National Key Research and Development Program of China under Grant 2019YFC1511401, the National Natural Science Foundation of China under Grant 61103038, and the National Natural Science Foundation of China under Grant 61103060.

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Authors

Contributions

Junfeng Xue: Writing, Editing, Simulation, Experiment, Idea generation; Shoukun Wang: Supervision and Finalizing; Junzheng Wang: Reviewing and Supervision; Zhihua Chen: Writing, Editing;Experiment; All authors read and approved the final manuscript.

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Correspondence to Zhihua Chen.

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Xue, J., Wang, S., Wang, J. et al. Stewart-Inspired Vibration Isolation Control for a Wheel-legged Robot via Variable Target Force Impedance Control. J Intell Robot Syst 106, 61 (2022). https://doi.org/10.1007/s10846-022-01757-3

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