Abstract
In this paper, the hybrid-triggered control (HTC) problem for nonlinear networked control systems with external disturbance is investigated by employing Takagi–Sugeno (T–S) fuzzy model. First of all, the observers are constructed to estimate system state and disturbance, respectively. With the help of disturbance estimation and attenuation (DEA) technique, the influence of external disturbance is attenuated effectively. Next, the HTC strategy is proposed to save the limited network resource while maintaining the desirable system performance. Then sufficient condition is proposed to ensure the exponential stability of the resultant closed-loop system, and the observer-based fuzzy controller is designed by solving an optimization problem. Finally, the effectiveness of the developed method is verified by a practical example.





Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Laghrouche, S., Liu, J., Ahmed, F.S., Harmouche, M., Wack, M.: Adaptive second-order sliding mode observer-based fault reconstruction for PEM fuel cell air-feed system. IEEE Trans. Control Syst. Technol. 23(3), 1098–1109 (2015)
He, W., Chen, Y., Yin, Z.: Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans. Cybern. 46(3), 620–629 (2016)
Zhou, Q., Shi, P., Tian, Y., Wang, M.: Approximation-based adaptive tracking control for MIMO nonlinear systems with input saturation. IEEE Trans. Cybern. 45(10), 2119–2128 (2015)
Wang, N., Gao, Y., Zhang, X.: Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle. IEEE Trans. Neural Netw. Learn. Syst. 32(12), 5456–5467 (2021)
Wang, N., Gao, Y., Zhao, H., Ahn, C.K.: Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle. IEEE Trans. Neural Netw. Learn. Syst. 32(7), 3034–3045 (2021)
Wang, N., Zhang, Y., Ahn, C.K., Xu, Q.: Autonomous pilot of unmanned surface vehicles: bridging path planning and tracking. IEEE Trans. Veh. Technol. 71(3), 2358–2374 (2022)
Wang, N., Ahn, C.K.: Coordinated trajectory-tracking control of a marine aerial-surface heterogeneous system. IEEE/ASME Trans. Mechatron. 26(6), 3198–3210 (2021)
Lu, R., Cheng, H., Bai, J.: Fuzzy-model-based quantized guaranteed cost control of nonlinear networked systems. IEEE Trans. Fuzzy Syst. 23(3), 567–575 (2015)
Yi, Y., Zheng, W.X., Sun, C., Guo, L.: DOB fuzzy controller design for non-Gaussian stochastic distribution systems using two-step fuzzy identification. IEEE Trans. Fuzzy Syst. 24(2), 401–418 (2016)
Xu, C., Zhang, Q., Wu, Y.: Existence and exponential stability of periodic solution to fuzzy cellular neural networks with distributed delays. Int. J. Fuzzy Syst. 18, 41–51 (2016)
Liu, J., et al.: Sliding mode control of grid-connected neutral-point-clamped converters via high-gain observer. IEEE Trans. Ind. Electron. 69(4), 4010–4021 (2022)
Liu, J., Wu, L., Wu, C., Luo, W., Franquelo, L.G.: Event-triggering dissipative control of switched stochastic systems via sliding mode. Automatica 103, 261–273 (2019)
Shi, P., Liu, M., Zhang, L.: Fault-tolerant sliding-mode-observer synthesis of Markovian jump systems using quantized measurements. IEEE Trans. Ind. Electron. 62(9), 5910–5918 (2015)
Wu, L., Su, X., Shi, P.: Sliding mode control with bounded \(L_{2}\) gain performance of Markovian jump singular time-delay systems. Automatica 48(8), 1929–1933 (2012)
Lin, Y., Nguyen, H.L.T.: Adaptive neuro-fuzzy predictor-based control for cooperative adaptive cruise control system. IEEE Trans. Intell. Transp. Syst. 21(3), 1054–1063 (2020)
Chiu, C., Ouyang, Y.: Robust maximum power tracking control of uncertain photovoltaic systems: a unified T–S fuzzy model-based approach. IEEE Trans. Control Syst. Technol. 19(6), 1516–1526 (2011)
Hagras, H.A.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2004)
Shanmugam, L., Joo, Y.H.: Stability and stabilization for T–S fuzzy large-scale interconnected power system with wind farm via sampled-data control. IEEE Trans. Syst. Man Cybern. Syst. 51(4), 2134–2144 (2021)
An, H., Liu, J., Wang, C., Wu, L.: Disturbance observer-based antiwindup control for air-breathing hypersonic vehicles. IEEE Trans. Ind. Electron. 63(5), 3038–3049 (2016)
Yang, J., Sun, J., Zheng, W.X., Li, S.: Periodic event-triggered robust output feedback control for nonlinear uncertain systems with time-varying disturbance. Automatica 94, 324–333 (2018)
Wang, C., Zuo, Z., Qi, Z., Ding, Z.: Predictor-based extended state observer design for consensus of mass with delays and disturbances. IEEE Trans. Cybern. 49(4), 1259–1269 (2019)
He, W., Zhang, S., Ge, S.S.: Boundary output-feedback stabilization of a Timoshenko beam using disturbance observer. IEEE Trans. Ind. Electron. 60(11), 5186–5194 (2013)
Zhang, J., Zheng, W.X., Xu, H., Xia, Y.: Observer-based event-driven control for discrete-time systems with disturbance rejection. IEEE Trans. Cybern. 51(4), 2120–2130 (2021)
Guo, B.Z., Zhao, Z.L.: On convergence of the nonlinear active disturbance rejection control for MIMO systems. SIAM J. Control Optim. 51(2), 1727–1757 (2013)
Xiao, F., Shi, Y., Chen, T.: Robust stability of networked linear control systems with asynchronous continuous and discrete time event-triggering schemes. IEEE Trans. Autom. Control 66(2), 932–939 (2021)
Liu, C., Li, H., Shi, Y., Xu, D.: Distributed event-triggered gradient method for constrained convex minimization. IEEE Trans. Autom. Control 65(2), 778–785 (2020)
Peng, C., Sun, H.: Switching-like event-triggered control for networked control systems under malicious denial of service attacks. IEEE Trans. Autom. Control 65(9), 3943–3949 (2020)
Garcia, A., Wang, L., Huang, J., Hong, L.: Distributed networked real-time learning. IEEE Trans. Control Netw. Syst. 8(1), 28–38 (2021)
Liu, J., Yin, Y., Luo, W., Vazquez, S., Franquelo, L.G., Wu, L.: Sliding mode control of a three-phase AC/DC voltage source converter under unknown load conditions: industry applications. IEEE Trans. Syst. Man Cybern. Syst. 48(10), 1771–1780 (2018)
Liu, J., Zha, L., Cao, J., Fei, S.: Hybrid-driven-based stabilization for networked control systems. IET Control Theory Appl. 10(17), 2279–2285 (2016)
Liu, J., Xia, J., Tian, E., Fei, S.: Hybrid-driven-based \(H_{\infty}\) filter design for neural networks subject to deception attacks. Appl. Math. Comput. 320, 158–174 (2018)
Liu, J., Gu, Y., Xie, X., Yue, D., Park, J.H.: Hybrid-driven-based \({\cal{H}}_{\infty}\) control for networked cascade control systems with actuator saturations and stochastic cyber attacks. IEEE Trans. Syst. Man Cybern. Syst. 49(12), 2452–2463 (2019)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61873147), the Foundation for Innovative Research Groups of National Natural Science Foundation of China (61821004), the Youth Innovation Group Project of Shandong University (2020QNQT016), and the Qilu Youth Scholar Project from Shandong University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, G., Yang, R. Observer-Based Hybrid-Triggered Control for Nonlinear Networked Control Systems with Disturbances. Int. J. Fuzzy Syst. 25, 316–325 (2023). https://doi.org/10.1007/s40815-022-01336-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-022-01336-6