Startseite Adaptive neural network control of second-order underactuated systems with prescribed performance constraints
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Adaptive neural network control of second-order underactuated systems with prescribed performance constraints

  • Can Ding EMAIL logo , Jing Zhang , Yingjie Zhang und Zhe Zhang ORCID logo
Veröffentlicht/Copyright: 12. August 2021
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

This paper studies the trajectory tracking control problem of second-order underactuated system subject to system uncertainties and prescribed performance constraints. By combining radial basis function neural networks (RBFNNs) with input–output linearization methods, an adaptive neural network-based control approach is proposed and the adaptive laws are given through Lyapunov method and Taylor expansion linearization approach. The main contributions of this paper are that: (1) by introducing weight performance function and transformation function, the states never violate the prescribed performance constraints; (2) the control scheme takes the unknown control gain direction into consideration and the singular problem of control design can be avoided; (3) through rigorously stability analysis, all signal of closed-loop system are proved to be uniformly ultimately bounded. The effectiveness of the proposed control scheme was verified by comparative simulation.


Corresponding author: Can Ding, College of Electrical and Information Engineering, Hunan University, Hunan 410082, China, E-mail:

Funding source: China Postdoctoral Science Special Foundation

Award Identifier / Grant number: 2021TQ0102

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This study was supported by China Postdoctoral Science Special Foundation (2021TQ0102).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] Y. Liu and H. Yu, “A survey of underactuated mechanical systems,” IET Control Theory & Appl., vol. 7, no. 7, pp. 921–935, 2013. https://doi.org/10.1049/iet-cta.2012.0505.Suche in Google Scholar

[2] X. Wu, K. Xu, M. Ma, et al.., “Output feedback control for an underactuated benchmark system with bounded torques,” Asian J. Control, 2020. https://doi.org/10.1002/asjc.2295.Suche in Google Scholar

[3] Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UAV,” IEEE Trans. Wireless Commun., vol. 18, no. 4, pp. 2329–2345, 2019. https://doi.org/10.1109/twc.2019.2902559.Suche in Google Scholar

[4] Y. Song, L. He, D. Zhang, et al.., “Neuroadaptive fault-tolerant control of quadrotor UAVs: a more affordable solution,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 7, pp. 1975–1983, 2018. https://doi.org/10.1109/TNNLS.2018.2876130.Suche in Google Scholar PubMed

[5] Z. Zhou, H. Wang, Z. Hu, et al.., “Distributed event-triggered consensus for multiple underactuated systems under Markovian switching topologies,” Asian J. Contr., vol. 22, no. 1, pp. 590–599, 2020. https://doi.org/10.1002/asjc.1864.Suche in Google Scholar

[6] L. Márton, A. S. Hodel, B. Lantos, et al.., “Underactuated robot control: comparing LQR, subspace stabilization, and combined error metric approaches,” IEEE Trans. Ind. Electron., vol. 55, no. 10, pp. 3724–3730, 2008. https://doi.org/10.1109/tie.2008.923285.Suche in Google Scholar

[7] J. Fei and C. Lu, “Adaptive sliding mode control of dynamic systems using double loop recurrent neural network structure,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 4, pp. 1275–1286, 2017. https://doi.org/10.1109/TNNLS.2017.2672998.Suche in Google Scholar PubMed

[8] F. Gao, W. Chen, Z. Li, et al.., “Neural network-based distributed cooperative learning control for multiagent systems via event-triggered communication,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 2, pp. 407–419, 2019. https://doi.org/10.1109/TNNLS.2019.2904253.Suche in Google Scholar PubMed

[9] G. Ruiz-García, H. Hagras, H. Pomares, et al.., “Toward a fuzzy logic system based on general forms of interval type-2 fuzzy sets,” IEEE Trans. Fuzzy Syst., vol. 27, no. 12, pp. 2381–2395, 2019. https://doi.org/10.1109/tfuzz.2019.2898582.Suche in Google Scholar

[10] J. Huang, M. Zhang, S. Ri, et al.., “High-order disturbance-observer-based sliding mode control for mobile wheeled inverted pendulum systems,” IEEE Trans. Ind. Electron., vol. 67, no. 3, pp. 2030–2041, 2019.10.1109/TIE.2019.2903778Suche in Google Scholar

[11] K. A. Rsetam, Z. Cao, and Z. Man, “Cascaded extended state observer based sliding mode control for underactuated flexible joint robot,” IEEE Trans. Ind. Electron., vol. 67, no. 12, pp. 10822–10832, 2019.10.1109/TIE.2019.2958283Suche in Google Scholar

[12] L. Chen, R. Cui, C. Yang, et al.., “Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: theory and experimental results,” IEEE Trans. Ind. Electron., vol. 67, no. 5, pp. 4024–4035, 2019.10.1109/TIE.2019.2914631Suche in Google Scholar

[13] D. Li, C. L. P. Chen, Y. J. Liu, et al.., “Neural network controller design for a class of nonlinear delayed systems with time-varying full-state constraints,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 9, pp. 2625–2636, 2019. https://doi.org/10.1109/tnnls.2018.2886023.Suche in Google Scholar

[14] M. M. Azimi and H. R. Koofigar, “Adaptive fuzzy backstepping controller design for uncertain underactuated robotic systems,” Nonlinear Dynam., vol. 79, no. 2, pp. 1457–1468, 2015.https://doi.org/10.1007/s11071-014-1753-y.Suche in Google Scholar

[15] S. Li, L. Ding, H. Gao, et al.., “Reinforcement learning neural network-based adaptive control for state and input time-delayed wheeled mobile robots,” IEEE Trans. Syst. Man Cybern. Syst., vol. 50, no. 11, pp. 4171–4182, 2018.10.1109/TSMC.2018.2870724Suche in Google Scholar

[16] L. Chen, R. Cui, C. Yang, et al.., “Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: theory and experimental results,” IEEE Trans. Ind. Electron., vol. 67, no. 5, pp. 4024–4035, 2019.10.1109/TIE.2019.2914631Suche in Google Scholar

[17] G. Zong, H. Ren, and H. R. Karimi, “Event-triggered communication and annular finite-time H∞ filtering for networked switched systems,” IEEE Trans. Cybern., no. 99, pp. 1–9, 2020. https://doi.org/10.1109/TCYB.2020.3010917.Suche in Google Scholar PubMed

[18] H. Ren, G. Zong, and H. Reza Karimi, “Asynchronous finite-time filtering of networked switched systems and its application: an event-driven method,” IEEE Trans. Circuits Syst. I Regul. Pap., vol. 66, no. 1, pp. 391–402, 2018.10.1109/TCSI.2018.2857771Suche in Google Scholar

[19] G. Zong, W. Qi, and H. R . L. Karimi, “Control of positive semi-Markov jump systems with state delay,” IEEE Trans. Syst. Man Cybern. Syst., no. 99, pp. 1–10, 2020. https://doi.org/10.1109/tsmc.2020.2980034.Suche in Google Scholar

[20] J. J. Zhang and Q. M. Sun, “Prescribed performance adaptive neural output feedback dynamic surface control for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics,” Int. J. Robust Nonlinear Cont., vol. 30, no. 2, pp. 459–483, 2020. https://doi.org/10.1002/rnc.4769.Suche in Google Scholar

[21] L. Liu, Y. J. Liu, A. Chen, et al.., “Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems,” Sci. China Inf. Sci., vol. 63, no. 3, pp. 1–14, 2020. https://doi.org/10.1007/s11432-019-2714-7.Suche in Google Scholar

[22] K. P. Tee, B. Ren, and S. S. Ge, “Control of nonlinear systems with time-varying output constraints,” Automatica, vol. 47, no. 11, pp. 2511–2516, 2011. https://doi.org/10.1016/j.automatica.2011.08.044.Suche in Google Scholar

[23] K. Zhao, Y. Song, T. Ma, et al.., “Prescribed performance control of uncertain Euler–Lagrange systems subject to full-state constraints,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 8, pp. 3478–3489, 2017. https://doi.org/10.1109/TNNLS.2017.2727223.Suche in Google Scholar PubMed

[24] L. Chen and Q. Wang, “Prescribed performance-barrier Lyapunov function for the adaptive control of unknown pure-feedback systems with full-state constraints,” Nonlinear Dynam., vol. 95, no. 3, pp. 2443–2459, 2019. https://doi.org/10.1007/s11071-018-4704-1.Suche in Google Scholar

[25] Y. J. Liu, S. Lu, D. Li, et al.., “Adaptive controller design-based ABLF for a class of nonlinear time-varying state constraint systems,” IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 7, pp. 1546–1553, 2016.10.1109/TSMC.2016.2633007Suche in Google Scholar

[26] Q. Zhou, L. Wang, C. Wu, et al.., “Adaptive fuzzy control for nonstrict-feedback systems with input saturation and output constraint,” IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 1, pp. 1–12, 2016.10.1109/TSMC.2016.2557222Suche in Google Scholar

[27] C. P. Bechlioulis and G. A. Rovithakis, “Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance,” IEEE Trans. Automat. Contr., vol. 53, no. 9, pp. 2090–2099, 2008. https://doi.org/10.1109/tac.2008.929402.Suche in Google Scholar

[28] C. P. Bechlioulis and G. A. Rovithakis, “Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems,” Automatica, vol. 45, no. 2, pp. 532–538, 2009. https://doi.org/10.1016/j.automatica.2008.08.012.Suche in Google Scholar

[29] Z. Zhikai, D. Guangren, and H. Mingzhe, “Longitudinal attitude control of a hypersonic vehicle with angle of attack constraints,” in 2015 10th Asian Control Conference (ASCC), IEEE, 2015, pp. 1–6.10.1109/ASCC.2015.7244862Suche in Google Scholar

[30] Q. Guo, Y. Zhang, B. G. Celler, et al.., “Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 12, pp. 3572–3583, 2018. https://doi.org/10.1109/TNNLS.2018.2854699.Suche in Google Scholar PubMed

[31] Y. J. Liu and H. Chen, “Adaptive sliding mode control for uncertain active suspension systems with prescribed performance,” IEEE Trans. Syst. Man Cybern. Syst., vol. 13, 2020. https://doi.org/10.1109/tsmc.2019.2961927.Suche in Google Scholar

[32] Z. Zhang, J. Zhang, and Z. Ai, “A novel stability criterion of the time-lag fractional-order gene regulatory network system for stability analysis,” Commun. Nonlinear Sci. Numer. Simulat., vol. 66, pp. 96–108, 2019. https://doi.org/10.1016/j.cnsns.2018.06.009.Suche in Google Scholar

[33] Z. Zhang, T. Ushio, Z. Ai, et al.., “Novel stability condition for delayed fractional-order composite systems based on vector Lyapunov function,” Nonlinear Dynam., vol. 99, no. 2, pp. 1253–1267, 2020.10.1007/s11071-019-05352-4Suche in Google Scholar

[34] Z. Zhang and J. Zhang, “Asymptotic stabilization of general nonlinear fractional-order systems with multiple time delays,” Nonlinear Dynam., vol. 102, no. 1, pp. 605–619, 2020. https://doi.org/10.1007/s11071-020-05943-6.Suche in Google Scholar

[35] J. Moreno-Valenzuela, C. Aguilar-Avelar, S. A. Puga-Guzmán, et al.., “Adaptive neural network control for the trajectory tracking of the Furuta pendulum,” IEEE Trans. Cybern., vol. 46, no. 12, pp. 3439–3452, 2016. https://doi.org/10.1109/tcyb.2015.2509863.Suche in Google Scholar

[36] A. M. Zou, Z. G. Hou, and M. Tan, “Adaptive control of a class of nonlinear pure-feedback systems using fuzzy backstepping approach,” IEEE Trans. Fuzzy Syst., vol. 16, no. 4, pp. 886–897, 2008. https://doi.org/10.1109/tfuzz.2008.917301.Suche in Google Scholar

[37] S. Sui, S. Tong, and Y. Li, “Adaptive fuzzy backstepping output feedback tracking control of MIMO stochastic pure-feedback nonlinear systems with input saturation,” Fuzzy Set Syst., vol. 254, pp. 26–46, 2014. https://doi.org/10.1016/j.fss.2014.03.013.Suche in Google Scholar

[38] F. Wang, Z. Liu, Y. Zhang, et al.., “Adaptive fuzzy control for a class of stochastic pure-feedback nonlinear systems with unknown hysteresis,” IEEE Trans. Fuzzy Syst., vol. 24, no. 1, pp. 140–152, 2015.10.1109/TFUZZ.2015.2446531Suche in Google Scholar

[39] M. M. Polycarpou and P. A. Ioannou, “A robust adaptive nonlinear control design,” in 1993 American Control Conference, IEEE, 1993, pp. 1365–1369.10.23919/ACC.1993.4793094Suche in Google Scholar

[40] Y. J. Liu and H. Chen, “Adaptive sliding mode control for uncertain active suspension systems with prescribed performance,” IEEE Trans. Syst. Man Cybern. Syst. vol. 13, 2020. https://doi.org/10.1109/tsmc.2019.2961927.Suche in Google Scholar

[41] Y. Li, S. Tong, L. Liu, et al.., “Adaptive output-feedback control design with prescribed performance for switched nonlinear systems,” Automatica, vol. 80, pp. 225–231, 2017. https://doi.org/10.1016/j.automatica.2017.02.005.Suche in Google Scholar

[42] Q. Guo, Y. Zhang, B. G. Celler, et al.., “Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 12, pp. 3572–3583, 2018. https://doi.org/10.1109/TNNLS.2018.2854699.Suche in Google Scholar PubMed

[43] W. Chen, S. S. Ge, J. Wu, et al.., “Globally stable Adaptive backstepping neural network control for uncertain strict-feedback systems with tracking accuracy known a priori,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 9, pp. 1842–1854, 2015. https://doi.org/10.1109/tnnls.2014.2357451.Suche in Google Scholar

[44] H. K. Khalil, Nonlinear Systems, Upper Saddle River, NJ, USA, Prentice-Hall, 2002.Suche in Google Scholar

Received: 2020-06-30
Accepted: 2021-07-20
Published Online: 2021-08-12
Published in Print: 2023-02-23

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Original Research Articles
  3. Modeling and assessment of the flow and air pollutants dispersion during chemical reactions from power plant activities
  4. Stochastic dynamics of dielectric elastomer balloon with viscoelasticity under pressure disturbance
  5. Unsteady MHD natural convection flow of a nanofluid inside an inclined square cavity containing a heated circular obstacle
  6. Fractional-order generalized Legendre wavelets and their applications to fractional Riccati differential equations
  7. Battery discharging model on fractal time sets
  8. Adaptive neural network control of second-order underactuated systems with prescribed performance constraints
  9. Optimal control for dengue eradication program under the media awareness effect
  10. Shifted Legendre spectral collocation technique for solving stochastic Volterra–Fredholm integral equations
  11. Modeling and simulations of a Zika virus as a mosquito-borne transmitted disease with environmental fluctuations
  12. Mathematical analysis of the impact of vaccination and poor sanitation on the dynamics of poliomyelitis
  13. Anti-sway method for reducing vibrations on a tower crane structure
  14. Stable soliton solutions to the time fractional evolution equations in mathematical physics via the new generalized G / G -expansion method
  15. Convergence analysis of online learning algorithm with two-stage step size
  16. An estimative (warning) model for recognition of pandemic nature of virus infections
  17. Interaction among a lump, periodic waves, and kink solutions to the KP-BBM equation
  18. Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications
  19. An efficient class of fourth-order derivative-free method for multiple-roots
  20. Numerical modeling of thermal influence to pollutant dispersion and dynamics of particles motion with various sizes in idealized street canyon
  21. Construction of breather solutions and N-soliton for the higher order dimensional Caudrey–Dodd–Gibbon–Sawada–Kotera equation arising from wave patterns
  22. Delay-dependent robust stability analysis of uncertain fractional-order neutral systems with distributed delays and nonlinear perturbations subject to input saturation
  23. Construction of complexiton-type solutions using bilinear form of Hirota-type
  24. Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization
  25. Infinite line of equilibriums in a novel fractional map with coexisting infinitely many attractors and initial offset boosting
  26. Lump solutions to a generalized nonlinear PDE with four fourth-order terms
  27. Quantum motion control for packaging machines
Heruntergeladen am 24.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/ijnsns-2020-0141/html
Button zum nach oben scrollen