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A Fuzzy Logic-based Cascade Control without Actuator Saturation for the Unmanned Underwater Vehicle Trajectory Tracking

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Abstract

An intelligent control strategy is proposed to eliminate the actuator saturation problem that exists in the trajectory tracking process of unmanned underwater vehicles (UUV). The control strategy consists of two parts: for the kinematic modeling part, a fuzzy logic-refined backstepping control is developed to achieve control velocities within acceptable ranges and errors of small fluctuations; on the basis of the velocities deducted by the improved kinematic control, the sliding mode control (SMC) is introduced in the dynamic modeling to obtain corresponding torques and forces that should be applied to the vehicle body. With the control velocities computed by the kinematic model and applied forces derived by the dynamic model, the robustness and accuracy of the UUV trajectory without actuator saturation can be achieved.

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References

  1. Gafurov, S., Klochkov, E.: Autonomous unmanned underwater vehicles development tendencies. Procedia Eng. 106, 141–148 (2015)

    Article  Google Scholar 

  2. Li, D., Wang, P., Du, L.: Path planning technologies for autonomous underwater vehicles-a review. IEEE Access 7, 9745–9768 (2019)

    Article  Google Scholar 

  3. Sun, P., Boukerche, A.: Modeling and analysis of coverage degree and target detection for autonomous underwater vehicle-based system. IEEE Trans. Veh. Technol. 67(10), 9959–9971 (2018)

    Article  Google Scholar 

  4. Bacha, S., Saadi, R., Ayad, M., Aboubou, A., Bahri, M.: A review on vehicle modeling and control technics used for autonomous vehicle path following. In: International conference on green energy conversion systems (GECS), Hammamet, Tunisia (2017)

  5. Liu, X., Zhang, M., Rogers, E.: Trajectory tracking control for autonomous underwater vehicles based on fuzzy re-planning of a local desired trajectory. IEEE Trans. Veh. Technol. 68(12), 11,657–11,667 (2019)

    Article  Google Scholar 

  6. Wang, X., Yao, X., Zhang, L.: Path planning under constraints and path following control of autonomous underwater vehicle with dynamical uncertainties and wave disturbances. J. Intell. Robot. Syst. 99(3-4), 891–908 (2020)

    Article  Google Scholar 

  7. Wan, L., Sun, N., Liao, Y.L.: Backstepping control method for the trajectory tracking for the underactuated autonomous underwater vehicle. Adv. Mat. Res. 798, 484–488 (2013). Qingdao,China

    Google Scholar 

  8. Karkoub, M., Wu, H.-M., Hwang, C.-L.: Nonlinear trajectory-tracking control of an autonomous underwater vehicle. Ocean Eng. 145, 188–198 (2017)

    Article  Google Scholar 

  9. Yang, S., Meng, M.-H.: Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach. IEEE Trans. Neural Netw. 14(6), 1541–1552 (2003)

    Article  Google Scholar 

  10. Sun, B., Zhang, W., Song, A., Zhu, X., Zhu, D.: Trajectory tracking and obstacle avoidance control of unmanned underwater vehicles based on Mpc. In: International conference on underwater system technology: theory and applications (USYS), Wuhan, China (2018)

  11. Hernandez-Sanchez, A., Chairez, I., Poznyak, A., Andrianova, O.: Dynamic motion backstepping control of underwater autonomous vehicle based on averaged sub-gradient integral sliding mode method. J. Intell. Robot. Syst. Theory Appl. vol. 103(3 (2021)

  12. Zhu, D., Sun, B.: The bio-inspired model based hybrid sliding-mode tracking control for unmanned underwater vehicles. Eng. Appl. Artif. Intell. 26(10), 2260–2269 (2013)

    Article  Google Scholar 

  13. Dong, L., Yan, J., Yuan, X., He, H., Sun, C.: Functional nonlinear model predictive control based on adaptive dynamic programming. IEEE Trans. Cybern. 49(12), 4206–4218 (2019)

    Article  Google Scholar 

  14. Luan, Z., Zhang, J., Zhao, W., Wang, C.: Trajectory tracking control of autonomous vehicle with random network delay. IEEE Trans. Veh. Technol. 69(8), 8140–8150 (2020)

    Article  Google Scholar 

  15. Gutierrez, B., Kwak, S.-S.: Modular multilevel converters (mmcs) controlled by model predictive control with reduced calculation burden. IEEE Trans. Power Electron. 33(11), 9176–9187 (2018)

    Article  Google Scholar 

  16. da Costa Sousa, J., Kaymak, U.: Model predictive control using fuzzy decision functions. IEEE Trans. Syst. Man Cybern. B Cybern. 31(1), 54–65 (2001)

    Article  Google Scholar 

  17. Na, J., Huang, Y., Wu, X., Su, S.-F., Li, G.: Adaptive finite-time fuzzy control of nonlinear active suspension systems with input delay. IEEE Trans. Cybern. 50(6), 2639–2650 (2020)

    Article  Google Scholar 

  18. Wang, F., Chen, B., Sun, Y., Gao, Y., Lin, C.: Finite-time fuzzy control of stochastic nonlinear systems. IEEE Trans. Cybern. 50(6), 2617–2626 (2020)

    Article  Google Scholar 

  19. Lee, C.: Fuzzy logic in control systems: fuzzy logic controller. i. IEEE Trans. Syst. Man Cybern. 20(2), 404–418 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  20. ——: Fuzzy logic in control systems: fuzzy logic controller. ii. IEEE Trans. Syst. Man Cybern. 20 (2), 419–435 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  21. Han, H., Wei, Y., Guan, L., Ye, X., Wang, A.: Trajectory tracking control of underwater vehicle-manipulator systems using uncertainty and disturbance estimator. In: Oceans MTS/IEEE Charleston, Charleston, SC USA (2018)

  22. Lun, G., Liu, Y., Yi, P., Qu, Y.: Design of dynamic control on underwater vehicle, Appl. Mech. Mater. 138-139, 333–338 (2012)

    Article  Google Scholar 

  23. Mohamed, S., Osman, A., Attia, S., Maged, S.: Dynamic model and control of an autonomous underwater vehicle. In: International conference on innovative trends in communication and computer engineering (ITCE), Aswan, Egypt, pp. 182–190 (2020)

  24. Yang, S., Meng, M.: Neural network approaches to dynamic collision-free trajectory generation. IEEE Trans. Syst. Man Cybern. B Cybern. 31(3), 302–318 (2001)

    Article  Google Scholar 

  25. Balcazar, R., De Jesus Rubio, J., Orozco, E., Andres cordova, D., Ochoa, G., Garcia, E., Pacheco, J., Gutierrez, G., Mujica-vargas, D., Aguilar-ibanez, C.: The regulation of an electric oven and an inverted pendulum. Symmetry 14(4), 759–782 (2022)

    Article  Google Scholar 

  26. Rubio, J.D.J., Orozco, E., Cordova, D.A., Islas, M.A., Pacheco, J., Gutierrez, G.J., Zacarias, A., Soriano, L.A., Meda-Campana, J.A., Mujica-Vargas, D.: Modified linear technique for the controllability and observability of robotic arms. IEEE Access 10, 3366–3377 (2022)

    Article  Google Scholar 

  27. Aguilar-Ibanez, C., Moreno-Valenzuela, J., Garcia-Alarcon, O., Martinez-Lopez, M., Acosta, J., Suarez-Castanon, M.: Pi-type controllers and - modulation for saturated dc-dc buck power converters. IEEE Access 9, 20,346–20,357 (2021)

    Article  Google Scholar 

  28. Soriano, L.A., Rubio, J.D.J., Orozco, E., Cordova, D.A., Ochoa, G., Balcazar, R., Cruz, D.R., Meda-campana, J.A., zacarias, A., Gutierrez, G.J.: Optimization of sliding mode control to save energy in a scara robot. Mathematics, (24) (2021)

  29. Soriano, L., Zamora, E., Vazquez-Nicolas, J., Hernandez, G., Barraza Madrigal, J., Balderas, D.: Pd control compensation based on a cascade neural network applied to a robot manipulator. Front. Neurorobot. 14, 577,749–577,757 (2020)

    Article  Google Scholar 

  30. Silva-Ortigoza, R., Hernandez-Marquez, E., Roldan-Caballero, A., Tavera-Mosqueda, S., Marciano-Melchor, M., Garcia-Sanchez, J., Hernandez-Guzman, V., Silva-Ortigoza, G.: Sensorless tracking control for a full-bridge buck inverter-dc motor system: passivity and flatness-based design. IEEE Access 9, 132,191–132,204 (2021)

    Article  Google Scholar 

  31. Li, T., Zhao, R., Chen, C.P., Fang, L., Liu, C.: Finite-time formation control of under-actuated ships using nonlinear sliding mode control. IEEE Trans. Cybern. 48(11), 3243–3253 (2018)

    Article  Google Scholar 

  32. Qin, J., Zhang, G., Zheng, W.X., Kang, Y.: Adaptive sliding mode consensus tracking for second-order nonlinear multiagent systems with actuator faults. IEEE Trans. Cybern. 49(5), 1605–1615 (2019)

    Article  Google Scholar 

  33. Zaihidee, F., Mekhilef, S., Mubin, M.: Robust speed control of pmsm using sliding mode control (smc)-a review. Energies 12(9), 1669–1696 (2019)

    Article  Google Scholar 

  34. Dhanasekar, R., Ganesh Kumar, S., Rivera, M.: Sliding mode control of electric drives/review. In: International conference on automatica (ICA-ACCA), Curico, Chile (2016)

  35. Liu, H., Zhang, T.: Fuzzy sliding mode control of robotic manipulators with kinematic and dynamic uncertainties. J Dyn. Syst-t. ASME, vol. 134(6) (2012)

  36. Rahmani, M., Rahman, M.H.: New hybrid control of autonomous underwater vehicles. Int. J. Control 94(11), 3038–3045 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhang, C., Wang, C., Wei, Y., Wang, J.: Neural network adaptive position tracking control of underactuated autonomous surface vehicle. J. Mech. Sci. Technol. 34(2), 855–865 (2020)

    Article  Google Scholar 

  38. Wang, N., Karimi, H.: Successive waypoints tracking of an underactuated surface vehicle. IEEE Trans. Ind. Inform. 16(2), 898–908 (2020)

    Article  Google Scholar 

  39. Wang, N., Pan, X.: Path following of autonomous underactuated ships: a translation-rotation cascade control approach. IEEE/ASME Trans. Mechatron. 24(6), 2583–2593 (2019)

    Article  Google Scholar 

  40. Hayashibe, M., Shimoda, S.: Synergetic learning control paradigm for redundant robot to enhance error-energy index. IEEE Trans. Con., Dev. Sys. 10(3), 573–584 (2018)

    Article  Google Scholar 

  41. Yongming, L., Shaocheng, T., Tieshan, L.: Composite adaptive fuzzy output feedback control design for uncertain nonlinear strict-feedback systems with input saturation. IEEE Trans. Cybern. 45(10), 2299–308 (2015)

    Article  Google Scholar 

  42. ——: Hybrid fuzzy adaptive output feedback control design for uncertain mimo nonlinear systems with time-varying delays and input saturation. IEEE Trans. Fuzzy Syst. 24(4), 841–53 (2016)

    Article  Google Scholar 

  43. Wang, H., Liu, P.X., Niu, B.: Robust fuzzy adaptive tracking control for nonaffine stochastic nonlinear switching systems. IEEE Trans. Cybern. 48(8), 2462–2471 (2018)

    Article  Google Scholar 

  44. Anderson, R.P., Bakolas, E., Milutinovi, D., Tsiotras, P.: Optimal feedback guidance of a small aerial vehicle in a stochastic wind. J. Guid. Control Dynamics 36(4), 975–985 (2013)

    Article  Google Scholar 

  45. Anderson, R.P., Milutinovi, D.: On the construction of minimum-time tours for a dubins vehicle in the presence of uncertainties. J. Dynamic Syst. Meas. Control Trans. ASME 137(3), 031,001–031,008 (2015)

    Article  Google Scholar 

  46. Omerdic, E., Roberts, G.: Thruster fault diagnosis and accommodation for open-frame underwater vehicles. Control Eng. Pract. 12(12), 1575–1598 (2004)

    Article  Google Scholar 

  47. Vervoort, J.: Modeling and Control of an Unmanned Underwater Vehicle. M.S. Thesis, Dept. Mech. Eng., Univ. of Canterbury, Christchurch, New Zealand (2009)

    Google Scholar 

  48. Zand, J.: Enhanced Navigation and Tether Management of Inspection Class Remotely Operated Vehicles Master’s Thesis. University of British Columbia, Canada (2005)

    Google Scholar 

  49. Shen, C., Shi, Y., Buckham, B.: Trajectory tracking control of an autonomous underwater vehicle using lyapunov-based model predictive control. IEEE Trans. Ind. Electron. 65(7), 5796–5805 (2018)

    Article  Google Scholar 

  50. Shtessel, Y., Foreman, D., Tournes, C.: Stability margins in traditional and second order sliding mode control. In: IEEE conference on decision and control, Orlando, FL, United states, Orlando, FL, United states, pp. 4604–4609 (2011)

  51. Soylu, S., Buckham, B.J., Podhorodeski, R.P.: A chattering-free sliding-mode controller for underwater vehicles with fault-tolerant infinity-norm thrust allocation. Ocean Eng. 35(16), 1647–1659 (2008)

    Article  Google Scholar 

  52. Mpanza, L., Pedro, J.: Nature-inspired optimization algorithms for sliding mode control parameters tuning for autonomous quadrotor. In: 2019 IEEE Conference on Control Technology and Applications (CCTA), Hong Kong, China, pp. 1087–1092 (2019)

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Acknowledgements

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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Authors and Affiliations

  1. Advanced Robotics & Intelligent System (ARIS) Laboratory and Advanced & Intelligent Control for Vehicles (AICV) Laboratory, School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada

    Danjie Zhu, Simon X. Yang & Mohammad Biglarbegian

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  1. Danjie Zhu
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  2. Simon X. Yang
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  3. Mohammad Biglarbegian
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Correspondence to Simon X. Yang.

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Zhu, D., Yang, S.X. & Biglarbegian, M. A Fuzzy Logic-based Cascade Control without Actuator Saturation for the Unmanned Underwater Vehicle Trajectory Tracking. J Intell Robot Syst 106, 39 (2022). https://doi.org/10.1007/s10846-022-01742-w

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