Abstract
In this paper, the design problem of intelligent ship autopilot with unmeasured yaw rate and input saturation is investigated based on adaptive fuzzy control. First, an adaptive fuzzy output feedback controller is proposed, then, to improve the convergence speed of the tracking error in the sense of finite time, a finite-time adaptive fuzzy output feedback controller is also designed to improve the control performance. By employing fuzzy logic system (FLS) to estimate the unknown nonlinear function, an adaptive fuzzy state observer is designed to estimate the unmeasured state. An adaptation auxiliary signal for input saturation is also established to compensate the mismatch between the controller signal and the actuator signal. Combining state observer model with backstepping method, two different adaptive fuzzy output-feedback controllers with composite parameter adaptive laws are constructed, respectively. Based on Lyapunov theory, it is proved that all signals in the closed-loop systems are bounded. Finally, the simulation results are conducted to demonstrate the effectiveness of the proposed schemes.
Similar content being viewed by others
References
Nejim, S.: Design of limited authority adaptive ship steering autopilots. Int. J. Adapt. Control Signal Process 14(4), 381–391 (2000)
Van, A.: Adaptive steering of ships: a model reference approach. Automatica 20(1), 3–14 (1984)
Tung, L.: Design a ship autopilot using neural network. J. Ship Prod. Des. 33(3), 192–196 (2017)
Sutton, R., Roberts, G., Taylor, S.: Tuning fuzzy ship autopilots using artificial neural networks. Trans. Inst. Meas. Control 19(2), 94–106 (1997)
Yang, Y., Ren, J.: Adaptive fuzzy robust tracking controller design via small gain approach and its application. IEEE Trans. Fuzzy Syst. 11(6), 783–795 (2003)
Yang, Y.: Model reference fuzzy adaptive control of ship course nonlinear system. Shipbuild. China 44(3), 85–93 (2003)
Bai, W., Zhou, Q., Li, T., Li, H.: Adaptive reinforcement learning neural network control for uncertain nonlinear system with input saturation. IEEE Trans. Cybern. (2019). https://doi.org/10.1109/TCYB.2019.2921057
Li, R., Cao, J., Li, T.: Active disturbance rejection control design and parameters conguration for ship steering with wave disturbance. Control Theory Appl. 35(11), 1601–1609 (2018)
Chang, C., Chang, W.: Robust fuzzy control with transient and steady-state performance constraints for ship fin stabilizing systems. Int. J. Fuzzy Syst. 21(2), 518–531 (2019)
Wang, N., Sun, Z., Su, S.: Fuzzy uncertainty observer-based path-following control of underactuated marine vehicles with unmodeled dynamics and disturbances. Int. J. Fuzzy Syst. 20(8), 2593–2604 (2018)
Wang, Z., Peng, X.: Control of ship course based on NN-adaptive output feedback. Trans. Beijing Inst. Technol. 31(4), 425–429 (2011)
Zheng, Y., Li, J., Li, R.: Output feedback control design for ship’s course keeping nonlinear system based on state observer. Proceedings of the 29th Chinese Control Conference, 606-609 (2010)
Peng, X., Hu, Z.: Adaptive nonlinear output feedback control with wave filter for ship course. Control Theory Appl. 30(7), 863–868 (2013)
Zheng, Z., Sun, L., Xie, L.: Error-constrained LOS path following of a surface vessel with actuator saturation and faults. IEEE Trans. Syst. Man Cybern. 48(10), 1794–1805 (2017)
Li, J., Li, T., Fan, Z., Bu, R., Hu, J.: Direct adaptive NN control of ship course autopilot with input saturation. The Fourth International Workshop on Advanced Computational Intelligence 655–661, (2012)
Qin, H., Chen, H., Sun, Y., Wu, Z.: The distributed adaptive finite-time chattering reduction containment control for multiple ocean bottom flying nodes. Int. J. Fuzzy Syst. 21(2), 607–619 (2019)
Wang, F., Chen, B., Liu, X., Lin, C.: Finite-time adaptive fuzzy tracking control design for nonlinear systems. IEEE Trans. Fuzzy Syst. 26(3), 1207–1216 (2018)
Khoo, S., Yin, J., Man, Z., Yu, X.: Finite-time stabilization of stochastic nonlinear systems in strict-feedback form. Automatica 49(5), 1403–1410 (2013)
Sun, Y., Chen, B., Lin, C., Wang, H.: Finite-time adaptive control for a class of nonlinear systems with nonstrict feedback structure. IEEE Trans. Cybern. 48(10), 2774–2782 (2018)
Li, T., Wang, D., Feng, G., Tong, S.: A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems. IEEE Trans. Syst. Man. Cybern. Part B 40(3), 915–927 (2010)
Wang, L.: Stable adaptive fuzzy control of nonlinear systems. IEEE Trans. Fuzzy Syst. 1(2), 146–155 (1993)
Zhu, Z., Xia, Y., Fu, M.: Attitude stabilization of rigid spacecraft with finite-time convergence. Int. J. Robust Nonlinear Control 21(6), 686–702 (2011)
Wang, F., Chen, B., Lin, C., Zhang, J., Meng, X.: Adaptive neural network finite-time output feedback control of quantized nonlinear systems. IEEE Trans. Cybern. 48(6), 1839–1848 (2018)
Tong, S., Min, X., Li, Y.: Observer-based adaptive fuzzy tracking control for strict-feedback nonlinear systems with unknown control gain functions. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.2977175
Tong, S., Sun, K., Sui, S.: Observer-based adaptive fuzzy decentralized optimal control design for strict-feedback nonlinear large-scale systems. IEEE Trans. Fuzzy Syst. 26(2), 569–584 (2018)
Qiu, J., Sun, K., Wang, T., Gao, H.: Observer-based fuzzy adaptive event-triggered control for pure-feedback nonlinear systems with prescribed performance. IEEE Trans. Fuzzy Syst. 27(11), 2152–2162 (2019)
Qian, C., Lin, W.: Non-lipschitz continuous stabilizers for nonlinear systems with uncontrollable unstable linearization. Syst. Control Lett. 42(3), 185–200 (2001)
Hardy, H., Littlewood, E., Polya, G.: Inequalities. Cambridge Univ. Press, Cambridge (1952)
Li, Y., Li, K., Tong, S.: Finite-time adaptive fuzzy output feedback dynamic surface control for mimo nonstrict feedback systems. IEEE Trans. Fuzzy Syst. 27(1), 96–110 (2019)
Zheng, Z., Huang, Y., Xie, L., Zhu, B.: Adaptive trajectory tracking control of a fully actuated surface vessel with asymmetrically constrained input and output. IEEE Trans. Control Syst. Technol. 26(5), 1851–1859 (2018)
Bai, W., Li, T., Tong, S.: NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.2963849
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 51939001, 61976033, U1813203, 61803064, 61751202, 61903092); the Science and Technology Innovation Funds of Dalian (under Grant No. 2018J11CY022); the Liaoning Revitalization Talents Program (under Grant Nos. XLYC1908018, XLYC1807046); the Natural Foundation Guidance Plan Project of Liaoning (2019-ZD-0151); the Fundamental Research Funds for the Central Universities (under Grant No. 3132019345); the Natural Science Foundation of Liaoning Province under Grant 20170540098.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhu, L., Li, T., Yu, R. et al. Observer-Based Adaptive Fuzzy Control for Intelligent Ship Autopilot with Input Saturation. Int. J. Fuzzy Syst. 22, 1416–1429 (2020). https://doi.org/10.1007/s40815-020-00880-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-020-00880-3