Abstract
In this paper, an output feedback scheme is proposed for a class of nonlinear systems with hysteresis input and sensor uncertainties. A hysteresis inverse function is applied to counteract the effect of the hysteresis input. The system nonlinearities and sensor uncertainties are dealt with by a dynamic gain, based on which a reduced-order observer and an output feedback controller are developed. It shows that the proposed scheme can globally stabilize the system. Finally, the effectiveness of the proposed scheme is verified by simulation.
Similar content being viewed by others
Data availability
This paper is theoretical and no data were used to support this study.
Notes
The specific deduction is shown in Appendix.
References
Zhang, S., He, X., Chen, Q.: Energy coupled-dissipation control for 3-dimensional overhead cranes. Nonlinear Dyn. 99(3), 2097–2107 (2020)
Xu, J., Lin, Z.: Low gain feedback for fractional-order linear systems and semi-global stabilization in the presence of actuator saturation. Nonlinear Dyn. 107(4), 3485–3504 (2022)
Zhang, S., Zhu, H., He, X., Feng, Y., Pang, C.K.: Passivity-based coupling control for underactuated three-dimensional overhead cranes. ISA Trans. 126, 352–360 (2022)
Zhai, J., Wang, H., Tao, J., He, Z.: Observer-based adaptive fuzzy finite time control for non-strict feedback nonlinear systems with unmodeled dynamics and input delay. Nonlinear Dyn. 111(2), 1417–1440 (2023)
Sun, Z.Y., Wang, M.: Disturbance attenuation via double-domination approach for feedforward nonlinear system with unknown output function. Nonlinear Dyn. 96(4), 2523–2533 (2019)
Sun, Z.Y., Xing, J.W., Meng, Q.: Output feedback regulation of time-delay nonlinear systems with unknown continuous output function and unknown growth rate. Nonlinear Dyn. 100(2), 1309–1325 (2020)
Li, H., Zhang, X., Li, M.: Design of output feedback controller for stochastic feedforward systems with unknown measurement sensitivity. ISA Trans. 97, 182–188 (2020)
Zhai, J., Qian, C.: Global control of nonlinear systems with uncertain output function using homogeneous domination approach. Int. J. Robust Nonlinear Control 22(14), 1543–1561 (2012)
Yan, X., Liu, Y., Zheng, W.X.: Global adaptive output-feedback stabilization for a class of uncertain nonlinear systems with unknown growth rate and unknown output function. Automatica 104, 173–181 (2019)
Chen, C.C., Qian, C., Sun, Z.Y., Liang, Y.W.: Global output feedback stabilization of a class of nonlinear systems with unknown measurement sensitivity. IEEE Trans. Autom. Control 63(7), 2212–2217 (2017)
Sun, Z.Y., Zhang, K., Chen, C.C., Meng, Q.: Robust output feedback control of time-delay nonlinear systems with dead-zone input and application to chemical reactor system. Nonlinear Dyn. 109(3), 1617–1627 (2022)
Zhang, J.X., Yang, G.H.: Global finite-time output stabilization of nonlinear systems with unknown measurement sensitivity. Int. J. Robust Nonlinear Control 28(16), 5158–5172 (2018)
Chang, Y., Zhang, X., Liu, S., Kong, L.: Event-triggered output feedback control for feedforward nonlinear systems with unknown measurement sensitivity. Nonlinear Dyn. 104(4), 3781–3791 (2021)
Zhang, X., Tan, J., Wu, J., Chen, W.: Event-triggered-based fixed-time adaptive neural fault-tolerant control for stochastic nonlinear systems under actuator and sensor faults. Nonlinear Dyn. 108(3), 2279–2296 (2022)
Liu, S., Su, C.Y.: Inverse error analysis and adaptive output feedback control of uncertain systems preceded with hysteresis actuators. IET Control Theory Appl. 8(17), 1824–1832 (2014)
Hua, C., Li, Y.: Output feedback prescribed performance control for interconnected time-delay systems with unknown Prandtl–Ishlinskii hysteresis. J. Franklin Inst. 352(7), 2750–2764 (2015)
Zhang, X., Wang, Y., Wang, C., Su, C.Y., Li, Z., Chen, X.: Adaptive estimated inverse output-feedback quantized control for piezoelectric positioning stage. IEEE Trans. Cybern. 49(6), 2106–2118 (2018)
Zhou, J., Wen, C., Li, T.: Adaptive output feedback control of uncertain nonlinear systems with hysteresis nonlinearity. IEEE Trans. Autom. Control 57(10), 2627–2633 (2012)
Chen, M., Ge, S.S.: Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer. IEEE Trans. Ind. Electron. 62(12), 7706–7716 (2015)
Zhang, X., Li, Z., Su, C.Y., Lin, Y., Fu, Y.: Implementable adaptive inverse control of hysteretic systems via output feedback with application to piezoelectric positioning stages. IEEE Trans. Ind. Electron. 63(9), 5733–5743 (2016)
Praly, L., Jiang, Z.P.: Linear output feedback with dynamic high gain for nonlinear systems. Syst. Control Lett. 53(2), 107–116 (2004)
Ma, F., Zhang, H., Bockstedte, A., Foliente, G.C., Paevere, P.: Parameter analysis of the differential model of hysteresis. J. Appl. Mech. 71(3), 342–349 (2004)
Sun, Z.Y., Xing, J.W., Chen, C.C.: Output feedback stabilization of time-delay nonlinear systems with unknown continuous time-varying output function and nonlinear growth rate. Int. J. Robust Nonlinear Control 30(6), 2579–2592 (2020)
Zhou, H., Zhai, J.: Universal adaptive control for a class of nonlinear time-varying delay systems with unknown output function. ISA Trans. 118, 66–74 (2021)
Yu, X., Lin, Y., Zhang, X.: Global adaptive output feedback tracking for a class of nonlinear systems with quantized input. J. Frankl. Inst. 357(10), 6083–6095 (2020)
Xue, L., Zhang, T., Zhang, W., Xie, X.J.: Global adaptive stabilization and tracking control for high-order stochastic nonlinear systems with time-varying delays. IEEE Trans. Autom. Control 63(9), 2928–2943 (2018)
Xie, J., Sun, P., Yang, D.: Adaptive fuzzy-based composite anti-disturbance control for a class of switched nonlinear systems with unknown backlash-like hysteresis. J. Frankl. Inst. 358(10), 5213–5236 (2021)
Sun, J., Yi, J., Pu, Z.: Fixed-time adaptive fuzzy control for uncertain nonstrict-feedback systems with time-varying constraints and input saturations. IEEE Trans. Fuzzy Syst. 30(4), 1114–1128 (2021)
Mei, K., Ding, S., Chen, C.C.: Fixed-time stabilization for a class of output-constrained nonlinear systems. IEEE Trans. Syst. Man Cybern. Syst. 52(10), 6498–6510 (2022)
Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant 51975002.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Proof of (33):
-
1.
Let
$$\begin{aligned} V_1 = \frac{1}{2}{z_1^2} + \frac{1}{2l}{z_1^2}. \end{aligned}$$(A.1)Thus,
$$\begin{aligned} \dot{V}_1&\le -{\rho _s}{\beta }{\vert z_1 \vert ^2} + 2 {\dot{\rho }_s}{\rho _s^{-1}}{\vert z_1 \vert ^2} + 2{\rho _s}{z_1}{z_2} + 2{l}{a_1}{\rho _s}{z_1^2} \nonumber \\&\quad + 2{\rho _s}{\varphi _1}{z_1} + 2\left( {\rho _s}{\varepsilon _1}-{\dot{\rho }_s}{\rho _s^{-1}}{f_s}+{\dot{f}_s}\right) {z_1} \nonumber \\&\quad \,- \frac{\dot{l}}{2l^2}{|z_1|^2} \nonumber \\&\le -{\frac{1}{2}}{\beta }{\vert z_1 \vert ^2} + \left( \frac{{\alpha _1}}{2}+\frac{45}{2\sigma _1}+{\frac{1}{d_1}}+3c+4M\right) {\vert z_1 \vert ^2} \nonumber \\&\quad \, + {\frac{1}{5}}{\sigma _1}{l}{\Vert \bar{e} \Vert ^2} + {\frac{1}{5}}{\sigma _1}{l}{\Vert \bar{\eta } \Vert ^2} - \frac{\alpha _2(y)}{2l}{|z_1|^2} + {d_2} \nonumber \\&\quad + {c}{|f_s|^2}, \end{aligned}$$(A.2)where
$$\begin{aligned}&\!\!\!2{\rho _s}{z_1}{z_2} + 2{l}{a_1}{\rho _s}{z_1^2} \le 2{l^{\frac{1}{2}}}{\rho _s}{z_1}({\bar{e}_1}+\bar{\eta }_1) \le \frac{45}{2\sigma _1}{\vert z_1 \vert ^2} \nonumber \\&\quad + {\frac{1}{5}}{l}{\sigma _1}{\Vert \bar{e} \Vert ^2} + {\frac{1}{5}}{l}{\sigma _1}{\Vert \bar{\eta } \Vert ^2}, \nonumber \\&2{\rho _s}{\varphi _1}{z_1} \le 2{c}{|z_1|^2} + 2{c}{|f_s|}{|z_1|} \le 3{c}{|z_1|^2} + {c}{|f_s|^2}, \nonumber \\&2\left( {\rho _s}{\varepsilon _1}-{\dot{\rho }_s}{\rho _s^{-1}}{f_s}+{\dot{f}_s}\right) {z_1} \le {\frac{1}{d_1}}{\vert z_1 \vert ^2} + {d_2}, \nonumber \\&-\frac{\dot{l}}{2l}{|z_1|^2} \le \frac{{\alpha _1}}{2}{|z_1|^2} - \frac{\alpha _2(y)}{2l}{|z_1|^2}. \end{aligned}$$(A.3) -
2.
Let
$$\begin{aligned} V_2 = {\sigma _3}{\bar{e}^\textrm{T}}{P_1}{\bar{e}}. \end{aligned}$$(A.4)So,
$$\begin{aligned} \dot{V}_2&\le -{\sigma _3}{\sigma _1}{l}{\Vert \bar{e} \Vert ^2} + 2{\sigma _3}{l^{\frac{1}{2}}}{\bar{\rho }}{\bar{e}^\textrm{T}}{P_1}{H}{z_2}+ 2{\sigma _3}{l^{\frac{3}{2}}}{a_1}{\bar{\rho }}{\bar{e}^\textrm{T}} \nonumber \\&\quad \quad \,\cdot {P_1}{H}{z_1}- 2{\sigma _3}{l^{\frac{1}{2}}}{\bar{\rho }}{\beta }{\bar{e}^\textrm{T}}{P_1}{H}{z_1} + 2{\sigma _3}{\bar{e}^\textrm{T}}{P_1}{L_3^{-1}}{\phi } \nonumber \\&\quad \quad \, - {\sigma _3}{\frac{\dot{l}}{l}}{\bar{e}^\textrm{T}}\left( {P_1}{D}+{D}{P_1}+ {P_1}\right) {\bar{e}} \nonumber \\&\le -\left( {\sigma _3}{\sigma _1}-{\sigma _3}{\sigma _2}{\alpha _1}{\Vert P_1 \Vert }-\frac{13}{10}{\sigma _1}\right) {l}{\Vert \bar{e} \Vert ^2} \nonumber \\&\quad \quad + {\frac{1}{5}}{\sigma _1}{l}{\Vert \bar{\eta } \Vert ^2} + \left( {\frac{5}{\sigma _1}}{\sigma _3^2}{\Vert P_1H \Vert ^2}+{\varPsi _3}\right) {|z_1|^2} \nonumber \\&\quad \quad + {\varPsi _1(y)}{\Vert \bar{e} \Vert ^2} + {\varPsi _2(y)}{\Vert \bar{\eta } \Vert ^2} - \frac{1}{2}{\sigma _3}{\lambda _1}{\alpha _2(y)}{\Vert \bar{e} \Vert ^2} \nonumber \\&\quad \quad \, + {d_3}, \end{aligned}$$(A.5)where
$$\begin{aligned}&2{\sigma _3}{l^{\frac{1}{2}}}{\bar{\rho }}{\bar{e}^\textrm{T}}{P_1}{H}{z_2} + 2{\sigma _3}{l^{\frac{3}{2}}}{a_1}{\bar{\rho }}{\bar{e}^\textrm{T}}{P_1}{H}{z_1} \nonumber \\&\quad \, \le 2{\sigma _3}{l}{\bar{\rho }}{\bar{e}^\textrm{T}}{P_1}{H}{(\bar{e}_1+\bar{\eta }_1)} \le 2{\sigma _3}{l}{|\bar{\rho }|}{\Vert P_1H \Vert }{\Vert \bar{e} \Vert ^2} \nonumber \\&\quad \quad + 2{\sigma _3}{l}{|\bar{\rho }|}{\Vert \bar{e} \Vert }{\Vert P_1H \Vert }{\Vert \bar{\eta } \Vert } \le {\frac{3}{5}}{\sigma _1}{l}{\Vert \bar{e} \Vert ^2} \nonumber \\&\quad \quad + {\frac{1}{5}}{\sigma _1}{l}{\Vert \bar{\eta } \Vert ^2}, -2{\sigma _3}{l^{\frac{1}{2}}}{\bar{\rho }}{\beta }{\bar{e}^\textrm{T}}{P_1}{H}{z_1} \nonumber \\&\qquad \le {\frac{1}{5}}{\sigma _1}{l}{\Vert \bar{e} \Vert ^2}+ {\frac{5}{\sigma _1}}{\sigma _3^2}{\Vert P_1H \Vert ^2}{|z_1|^2}, \nonumber \\&|\phi _2| \le {c}{\rho _s^{-1}}{|z_1|} + {c}{\rho _s^{-1}}{|f_s|} + {\gamma _2(y)}{l^{\frac{1}{2}}}{|\bar{e}_1+\bar{\eta }_1|} \nonumber \\&\quad \quad \, + {\gamma _2(y)}{\beta }{|z_1|} + {l}{a_1}{c}{|z_1|} + {l}{a_1}{c}{|f_s|} + {|\varepsilon _2|} \nonumber \\&\quad \quad \, + {l}{a_1}{\rho _s^{-1}}{|\dot{\rho }_s|}{|z_1|} + {l}{a_1}{\vert {{\rho _s}{\varepsilon _1}}-{{\dot{\rho }_s}{\rho _s^{-1}}{f_s}}+{\dot{f}_s}\vert } \nonumber \\&\quad \quad \, + {\rho _s}{\beta ^2}{|z_1|} + {\beta }{|\dot{\rho }_s|}{\rho _s^{-1}}{|z_1|} + {l^{\frac{1}{2}}}{\rho _s}{\beta }{|\bar{e}_1+\bar{\eta }_1|} \nonumber \\&\quad \quad + {c}{\rho _s}{\beta }{|z_1|} + {\beta }{\vert {{\rho _s}{\varepsilon _1}}-{{\dot{\rho }_s}{\rho _s^{-1}}{f_s}}+{\dot{f}_s}\vert }, \nonumber \\&|\phi _{i+1}| \le {c}{\rho _s^{-1}}{|z_1|} + {c}{\rho _s^{-1}}{|f_s|} + {\gamma _i(y)}\sum _{j=1}^{i}{l^{j-\frac{1}{2}}}{|\bar{e}_j+\bar{\eta }_j|} \nonumber \\&\quad + {\gamma _i(y)}{\beta }{|z_1|} + {l^i}{a_i}{c}{|z_1|} +{l^i}{a_i}{c}{|f_s|} + {|\varepsilon _{i+1}|} \nonumber \\&\quad \, + {l^i}{a_i}{\rho _s^{-1}}{|\dot{\rho }_s|}{|z_1|} + {l^i}{a_i}{\vert {{\rho _s}{\varepsilon _1}}-{{\dot{\rho }_s}{\rho _s^{-1}}{f_s}}+{\dot{f}_s}\vert }, \nonumber \\&2{\sigma _3}{\bar{e}^\textrm{T}}{P_1}{L_3^{-1}}{\phi } \le 2{\sigma _3}{\Vert P_1 \Vert }{\Vert \bar{e} \Vert }{\sum _{i=1}^{n-1}}{l^{-i+\frac{1}{2}}}{\phi _{i+1}} \nonumber \\&\quad \le {\varPsi _1(y)}{\Vert \bar{e} \Vert ^2} + {\varPsi _2(y)}{\Vert \bar{\eta } \Vert ^2} + {\varPsi _3}{|z_1|^2} + \frac{1}{2}{\sigma _1}{l}{\Vert \bar{e} \Vert ^2} \nonumber \\&\qquad + {d_3}, \nonumber \\&\quad -{\sigma _3}{\frac{\dot{l}}{l}}{\bar{e}^\textrm{T}}\left( {P_1}{D}+{D}{P_1}+ {P_1}\right) {\bar{e}} \nonumber \\&\quad \le {\sigma _3}{\sigma _2}{\alpha _1}{l}{\bar{e}^\textrm{T}}{P_1}{\bar{e}} - \frac{1}{2}{\sigma _3}{\alpha _2(y)}{\bar{e}^\textrm{T}}{P_1}{\bar{e}} \nonumber \\&\quad \le {\sigma _3}{\sigma _2}{\alpha _1}{l}{\Vert P_1 \Vert }{\Vert \bar{e} \Vert ^2} - \frac{1}{2}{\sigma _3}{\lambda _1}{\alpha _2(y)}{\Vert \bar{e} \Vert ^2}, \end{aligned}$$(A.6)and \( \lambda _1 \) is the minimum eigenvalue of \( P_1 \).
-
3.
Let
$$\begin{aligned} V_3 = {\bar{\eta }^\textrm{T}}{P_2}{\bar{\eta }}. \end{aligned}$$(A.7)
Therefore,
where
and \( \lambda _2 \) is the minimum eigenvalue of \( P_2 \).
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, Y., Chen, W., Lin, Y. et al. Output feedback stabilization for a class of nonlinear systems with hysteresis input and sensor uncertainties. Nonlinear Dyn 111, 17193–17203 (2023). https://doi.org/10.1007/s11071-023-08766-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-023-08766-3