Top

Automatic Control and Computer Sciences

Published in:

01-02-2024

Fixed Time Adaptive Fuzzy Control of Nonlinear Systems with Time-Varying State Constraints

Authors: Kexin Ding, Xueliang Zhang, Yurong Nan, Min Zhuang

Published in: Automatic Control and Computer Sciences | Issue 1/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this paper, a fixed-time adaptive fuzzy control scheme is investigated to stabilize a class of uncertain nonlinear systems with full-state constraints. A novel secant barrier Lyapunov function (SBLF) is first constructed to design the controller and obtain the fixed-time stability properties by using the (SBLF) in the design process of back-stepping. The adaptive controller is presented to guarantee that the tracking errors of the system can converge into the neighborhood around the equilibrium point in a fixed time and all the system states can be restricted within the predefined time-varying boundaries. By making use of Lyapunov analysis, we can prove that all the signals in the closed loop system are uniformly ultimately bounded and the output is well driven to follow the desired trajectory. Finally, simulations are given to verify the effectiveness of the method.

previous article Sensors and Machine Learning Algorithms for Location and POSTURE Activity Recognition in Smart Environments

next article Analysis of Employment Information of University Graduates through Data Mining

He, W., Kong, L., Dong, Yi., Yu, Ya., Yang, C., and Sun, C., Fuzzy tracking control for a class of uncertain MIMO nonlinear systems with state constraints, IEEE Trans. Syst., Man, Cybern.: Syst., 2019, vol. 49, no. 3, pp. 543–554. https://doi.org/10.1109/tsmc.2017.2749124CrossRef

Liu, Ya.-J., Li, J., Tong, S., and Chen, C.L.P., Neural network control-based adaptive learning design for nonlinear systems with full-state constraints, IEEE Trans. Neural Networks Learn. Syst., 2016, vol. 27, no. 7, pp. 1562–1571. https://doi.org/10.1109/tnnls.2015.2508926MathSciNetCrossRef

Meng, W., Yang, Q., and Sun, Y., Adaptive neural control of nonlinear MIMO systems with time-varying output constraints, IEEE Trans. Neural Networks Learn. Syst., 2014, vol. 26, no. 5, pp. 1074–1085. https://doi.org/10.1109/TNNLS.2014.2333878MathSciNetCrossRef

Ilchmann, A., Ryan, E.P., and Trenn, S., Tracking control: Performance funnels and prescribed transient behaviour, Syst. Control Lett., 2005, vol. 54, no. 7, pp. 655–670. https://doi.org/10.1016/j.sysconle.2004.11.005MathSciNetCrossRef

Bechlioulis, C.P. and Rovithakis, G.A., Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance, IEEE Trans. Autom. Control, 2008, vol. 53, no. 9, pp. 2090–2099. https://doi.org/10.1109/tac.2008.929402MathSciNetCrossRef

Ngo, K.B., Mahony, R., and Jiang, Zh.-P., Integrator backstepping using barrier functions for systems with multiple state constraints, Proc. 44th IEEE Conf. on Decision and Control, Seville, 2005, IEEE, 2005, pp. 8306–8312. https://doi.org/10.1109/cdc.2005.1583507

Zheng, Z., Huang, Ya., Xie, L., and Zhu, B., Adaptive trajectory tracking control of a fully actuated surface vessel with asymmetrically constrained input and output, IEEE Trans. Control Syst. Technol., 2017, vol. 26, no. 5, pp. 1851–1859. https://doi.org/10.1109/tcst.2017.2728518ADSCrossRef

Tang, Zh.-L., Ge, S.S., Tee, K.P., and He, W., Robust adaptive neural tracking control for a class of perturbed uncertain nonlinear systems with state constraints, IEEE Trans. Syst., Man, Cybern.: Syst., 2016, vol. 46, no. 12, pp. 1618–1629. https://doi.org/10.1109/tsmc.2015.2508962CrossRef

Xu, J.-X. and Jin, X., State-constrained iterative learning control for a class of MIMO systems, IEEE Trans. Autom. Control, 2013, vol. 58, no. 5, pp. 1322–1327. https://doi.org/10.1109/tac.2012.2223353CrossRef

10.

Zhou, J., Wen, C., and Li, T., Adaptive output feedback control of uncertain nonlinear systems with hysteresis nonlinearity, IEEE Trans. Autom. Control, 2012, vol. 57, no. 10, pp. 2627–2633. https://doi.org/10.1109/tac.2012.2190208MathSciNetCrossRef

11.

Li, T., Li, Z., Wang, D., and Chen, C.L.P., Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control directions, IEEE Trans. Neural Networks Learn. Syst., 2014, vol. 26, no. 6, pp. 1188–1201. https://doi.org/10.1109/tnnls.2014.2334638MathSciNetCrossRef

12.

Chen, Q., Shi, L., Na, J., Ren, X., and Nan, Yu., Adaptive echo state network control for a class of pure-feedback systems with input and output constraints, Neurocomputing, 2018, vol. 275, pp. 1370–1382. https://doi.org/10.1016/j.neucom.2017.09.083CrossRef

13.

Fu, C., Yu, J., Zhao, L., Yu, H., Lin, C., and Ma, Yu., Barrier Lyapunov function-based adaptive fuzzy control for induction motors with iron losses and full state constraints, Neurocomputing, 2018, vol. 287, pp. 208–220. https://doi.org/10.1016/j.neucom.2018.02.020CrossRef

14.

Wang, F., Chen, B., Liu, X., and Lin, C., Finite-time adaptive fuzzy tracking control design for nonlinear systems, IEEE Trans. Fuzzy Syst., 2017, vol. 26, no. 3, pp. 1207–1216. https://doi.org/10.1109/tfuzz.2017.2717804CrossRef

15.

Hou, S., Fei, J., Chen, C., and Chu, Yu., Finite-time adaptive fuzzy-neural-network control of active power filter, IEEE Trans. Power Electron., 2019, vol. 34, no. 10, pp. 10298–10313. https://doi.org/10.1109/tpel.2019.2893618ADSCrossRef

16.

Xia, J., Zhang, J., Sun, W., Zhang, B., and Wang, Z., Finite-time adaptive fuzzy control for nonlinear systems with full state constraints, IEEE Trans. Syst., Man, Cybern.: Syst., 2018, vol. 49, no. 7, pp. 1541–1548. https://doi.org/10.1109/tsmc.2018.2854770CrossRef

17.

Jin, X., Adaptive fixed-time control for MIMO nonlinear systems with asymmetric output constraints using universal barrier functions, IEEE Trans. Autom. Control, 2018, vol. 64, no. 7, pp. 3046–3053. https://doi.org/10.1109/TAC.2018.2874877MathSciNetCrossRef

18.

Tao, M., Chen, Q., He, X., and Sun, M., Adaptive fixed-time fault-tolerant control for rigid spacecraft using a double power reaching law, Int. J. Robust Nonlinear Control, 2019, vol. 29, no. 12, pp. 4022–4040. https://doi.org/10.1002/rnc.4593MathSciNetCrossRef

19.

Jiang, B., Hu, Q., and Friswell, M.I., Fixed-time attitude control for rigid spacecraft with actuator saturation and faults, IEEE Trans. Control Syst. Technol., 2016, vol. 24, no. 5, pp. 1892–1898. https://doi.org/10.1109/tcst.2016.2519838CrossRef

20.

Ge, S.S. and Wang, C., Direct adaptive NN control of a class of nonlinear systems, IEEE Trans. Neural Networks, 2002, vol. 13, no. 1, pp. 214–221. https://doi.org/10.1109/72.977306CrossRefPubMed

21.

Ma, J., Wang, J., Fei, S., and Liu, Ya., Global fixed-time control for nonlinear systems with unknown control coefficients and dead-zone input, IEEE Trans. Circuits Syst. II: Express Briefs, 2022, vol. 69, no. 2, pp. 594–598. https://doi.org/10.1109/tcsii.2021.3096523CrossRef

22.

Jia, T., Pan, Yi., Liang, H., and Lam, H., Event-based adaptive fixed-time fuzzy control for active vehicle suspension systems with time-varying displacement constraint, IEEE Trans. Fuzzy Syst., 2022, vol. 30, no. 8, pp. 2813–2821. https://doi.org/10.1109/tfuzz.2021.3075490CrossRef

23.

Wang, C., Wang, M., Liu, T., and Hill, D., Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems, IEEE Trans. Neural Network Learn. Syst., 2012, vol. 23, no. 10, pp. 1539–1550. https://doi.org/10.1109/TNNLS.2012.2205702CrossRef

24.

Yu, J., Shi, P., Dong, W., and Lin, C., Adaptive fuzzy control of nonlinear systems with unknown dead zones based on command filtering, IEEE Trans. Fuzzy Syst., 2016, vol. 26, no. 1, pp. 46–55. https://doi.org/10.1109/tfuzz.2016.2634162ADSCrossRef

Title: Fixed Time Adaptive Fuzzy Control of Nonlinear Systems with Time-Varying State Constraints
Authors: Kexin Ding
Xueliang Zhang
Yurong Nan
Min Zhuang
Publication date: 01-02-2024
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 1/2024
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S014641162401005X

Springer Professional

Abstract

Please log in to get access to your license.

Other articles of this Issue 1/2024

Elaborated Analysis of a Nonreplenishable Queue with Erlang Distribution of the Service Time

Research on Multisensor Data Fusion Model for Monitoring Animal Breeding Environment

Monostatic Pulsed Ultrawideband Radar Antenna for Studying Biological Rhythms of the Human Body and Internal Inhomogeneities of Dielectric Objects

Eamlm: Enhanced Automated Machine Learning Model for IoT Based Water Quality Analysis with Real-Time Dataset

Research on GDR Obstacle Detection Method Based on Stereo Vision

Analysis of Employment Information of University Graduates through Data Mining