Skip to main content
Log in

Observer-based adaptive fuzzy finite-time prescribed performance tracking control for strict-feedback systems with input dead-zone and saturation

  • Original paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This paper considers the problems of finite-time prescribed performance tracking control for a class of strict-feedback nonlinear systems with input dead-zone and saturation simultaneously. The unknown nonlinear functions are approximated by fuzzy logic systems and the unmeasurable states are estimated by designing a fuzzy state observer. In addition, a non-affine smooth function is used to approximate the non-smooth input dead-zone and saturated nonlinearity, and it is varied to the affine form via the mean value theorem. An adaptive fuzzy output feedback controller is developed by backstepping control method and Nussbaum gain method. It guarantees that the tracking error falls within a pre-set boundary at finite time and all the signals of the closed-loop system are bounded. The simulation results illustrate the feasibility of the design scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Wang, L.X.: Adaptive fuzzy systems and control: design and stability analysis. J. Intell. Fuzzy Syst. 3(2), 187–188 (1995)

    Article  Google Scholar 

  2. Huaguang, Z., Cai, L., Bien, Z.: A fuzzy basis function vector-based multivariable adaptive controller for nonlinear systems. IEEE Trans. Syst. Man Cybern. B Cybern. 30(1), 210–217 (2000)

    Article  Google Scholar 

  3. Wang, H., Liu, X., Chen, B., Zhou, Q.: Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems. Nonlinear Dyn. 75(3), 449–460 (2014)

    Article  MathSciNet  Google Scholar 

  4. Li, Z., Ding, L., Gao, H., Duan, G., Su, C.Y.: Trilateral teleoperation of adaptive fuzzy force motion control for nonlinear teleoperators with communication random delays. IEEE Trans. Fuzzy Syst. 21(4), 610–624 (2012)

    Article  Google Scholar 

  5. Kumpati, S.N., Kannan, P.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990)

    Article  Google Scholar 

  6. Ho, D.W., Li, J., Niu, Y.: Adaptive neural control for a class of nonlinearly parametric time-delay systems. IEEE Trans. Neural Netw. 16(3), 625–635 (2005)

    Article  Google Scholar 

  7. Ge, S.S., Tee, K.P.: Approximation-based control of nonlinear mimo time-delay systems. Automatica 43(1), 31–43 (2007)

    Article  MathSciNet  Google Scholar 

  8. Wu, L.B., Park, J.H.: Adaptive fault-tolerant control of uncertain switched nonaffine nonlinear systems with actuator faults and time delays. IEEE Trans. Syst. Man Cybern. Syst. 50(9), 3470–3480 (2020)

  9. Wu, L.B., Park, J.H., Zhao, N.N.: Robust adaptive fault-tolerant tracking control for nonaffine stochastic nonlinear systems with full-state constraints. IEEE Trans. Cybern. 50(8), 3793–3805 (2020)

  10. Chen, M., Ge, S.S., How, B.V.E.: Robust adaptive neural network control for a class of uncertain mimo nonlinear systems with input nonlinearities. IEEE Trans. Neural Netw. 21(5), 796–812 (2010)

    Article  Google Scholar 

  11. Wu, C., Liu, J., Xiong, Y., Wu, L.: Observer-based adaptive fault-tolerant tracking control of nonlinear nonstrict-feedback systems. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 3022–3033 (2017)

    MathSciNet  Google Scholar 

  12. 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. 50(9), 3903–3913 (2020)

  13. Tong, S., Li, Y.: Observer-based fuzzy adaptive control for strict-feedback nonlinear systems. Fuzzy Sets Syst. 160(12), 1749–1764 (2009)

    Article  MathSciNet  Google Scholar 

  14. Chen, B., Liu, X., Lin, C.: Observer and adaptive fuzzy control design for nonlinear strict-feedback systems with unknown virtual control coefficients. IEEE Trans. Fuzzy Syst. 26(3), 1732–1743 (2017)

    Article  Google Scholar 

  15. Li, Y., Tong, S., Li, T.: Observer-based adaptive fuzzy tracking control of mimo stochastic nonlinear systems with unknown control directions and unknown dead zones. IEEE Trans. Fuzzy Syst. 23(4), 1228–1241 (2014)

    Article  Google Scholar 

  16. Bechlioulis, C.P., Rovithakis, G.A.: Prescribed performance adaptive control of siso feedback linearizable systems with disturbances. In: 2008 16th Mediterranean Conference on Control and Automation IEEE. pp. 1035–1040 (2008)

  17. Bechlioulis, C.P., Rovithakis, G.A.: Robust adaptive control of feedback linearizable mimo nonlinear systems with prescribed performance. IEEE Trans. Autom. Control 53(9), 2090–2099 (2008)

    Article  MathSciNet  Google Scholar 

  18. Ryan, E.P., Sangwin, C., Townsend, P.: Controlled functional differential equations: approximate and exact asymptotic tracking with prescribed transient performance. ESAIM Control Optim. CA 15(4), 745–762 (2009)

    Article  MathSciNet  Google Scholar 

  19. Mathiyalagan, K., Sangeetha, G.: Finite-time stabilization of nonlinear time delay systems using lqr based sliding mode control. J. Frankl Inst. 356(7), 3948–3964 (2019)

    Article  MathSciNet  Google Scholar 

  20. Mathiyalagan, K., Balasubramani, M., Chang, X.H., Sangeetha, G.: Finite-time dissipativity-based filter design for networked control systems. Int. J. Adapt. Control Signal Process. 33(11), 1706–1721 (2019)

    Article  MathSciNet  Google Scholar 

  21. Liu, Y., Liu, X., Jing, Y.: Adaptive neural networks finite-time tracking control for non-strict feedback systems via prescribed performance. Inf. Sci. 468, 29–46 (2018)

    Article  MathSciNet  Google Scholar 

  22. 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 (2018)

    Article  Google Scholar 

  23. Li, Y., Li, K., Tong, S.: Adaptive neural network finite-time control for multi-input and multi-output nonlinear systems with positive powers of odd rational numbers. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2532–2543 (2020)

    MathSciNet  Google Scholar 

  24. Wang, F., Chen, B., Sun, Y., Gao, Y., Lin, C.: Finite-time fuzzy control of stochastic nonlinear systems. IEEE Trans. Cybern. 50(6), 2617–2626 (2020)

  25. Wang, F., Zhang, X.: Adaptive finite-time control of nonlinear systems under time-varying actuator failures. IEEE Trans. Syst. Man Cybern. Syst. 49(9), 1845–1852 (2018)

    Article  Google Scholar 

  26. Wu, Y., Zhang, G., Wu, L., Hu, W.: Observer-based finite time adaptive fault tolerant control for nonaffine nonlinear systems with actuator faultsand disturbances. Int. J. Adapt. Control Signal Process. 34, 1430–1446 (2020)

    Article  Google Scholar 

  27. Li, K., Tong, S.: Observer-based finite-time fuzzy adaptive control for MIMO non-strict feedback nonlinear systems with errors constraint. Neurocomputing 341, 135–148 (2019)

    Article  Google Scholar 

  28. Liu, Y.J., Tong, S.: Adaptive nn tracking control of uncertain nonlinear discrete-time systems with nonaffine dead-zone input. IEEE Trans. Cybern. 45(3), 497–505 (2014)

    Article  Google Scholar 

  29. Selvaraj, P., Sakthivel, R., Kwon, O.M.: Synchronization of fractional-order complex dynamical network with random coupling delay, actuator faults and saturation. Nonlinear Dyn. 94(4), 3101–3116 (2018)

    Article  Google Scholar 

  30. Chen, Z., Li, Z., Chen, C.P.: Disturbance observer-based fuzzy control of uncertain mimo mechanical systems with input nonlinearities and its application to robotic exoskeleton. IEEE Trans. Cybern. 47(4), 984–994 (2016)

    Article  Google Scholar 

  31. Galuppini, G., Magni, L., Raimondo, D.M.: Model predictive control of systems with deadzone and saturation. Control Eng. Pract. 78, 56–64 (2018)

    Article  Google Scholar 

  32. Wang, B., Li, S., Chen, Q.: Robust adaptive finite-time tracking control of uncertain mechanical systems with input saturation and deadzone. Trans. Ins. Meas. Control. 41(2), 560–572 (2019)

    Article  Google Scholar 

  33. Liu, C., Wang, H., Liu, X., Zhou, Y.: Adaptive fuzzy funnel control for nonlinear systems with input deadzone and saturation. Int. J. Syst. Sci. 1, 1–14 (2020)

    MathSciNet  Google Scholar 

  34. Wang, L.X.: Stable adaptive fuzzy control of nonlinear systems. IEEE Trans. Fuzzy Syst. 1(2), 146–155 (1993)

    Article  Google Scholar 

  35. Ye, X., Jiang, J.: Adaptive nonlinear design without a priori knowledge of control directions. IEEE Trans. Autom. Control 43(11), 1617–1621 (1998)

    Article  MathSciNet  Google Scholar 

  36. Mudgett, D.R., Morse, A.S.: Adaptive stabilization of linear systems with unknown high-frequency gains. IEEE Trans. Autom. Control 30(6), 549–554 (1984)

    Article  MathSciNet  Google Scholar 

  37. Zhang, T.P., Ge, S.S.: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica 44(7), 1895–1903 (2008)

    Article  MathSciNet  Google Scholar 

  38. Wen, C., Zhou, J., Liu, Z., Su, H.: Robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance. IEEE Trans. Autom. Control 56(7), 1672–1678 (2011)

    Article  MathSciNet  Google Scholar 

  39. Wang, H., Liu, P.X., Xie, X., Liu, X., Hayat, T., Alsaadi, F.E.: Adaptive fuzzy asymptotical tracking control of nonlinear systems with unmodeled dynamics and quantized actuator. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.04.011

    Article  Google Scholar 

  40. Liu, Y.J., Tong, S.: Barrier lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints. Automatica 64, 70–75 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the Scientific Research Fund of Liaoning Provincial Education Department of China (Grant No. 2020LNZD05) and Shandong Key Laboratory of Intelligent Buildings Technology (Grant No. SDIBT202002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chuang Gao.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A

The derivations of (28) are given as follows:

From (15)–(24), the time derivative of e is described as

$$\begin{aligned} {\dot{e}}= & {} {\dot{x}}-{\dot{\eta }} \nonumber \\= & {} A\left( x-\eta \right) +\sum _{i=1}^{n}H_{i}\left( f\left( {\bar{\eta }} _{i}\right) +\varDelta F_{i}-{\hat{f}}_{i}\left( {\bar{\eta }}_{i}|\theta _{i}\right) \right) \nonumber \\= & {} Ae+\sum _{i=1}^{n}H_{i}\left( {\hat{f}}_{i}\left( {\bar{\eta }}_{i}|\theta _{i}^{*}\right) +\varepsilon _{i}+\varDelta F_{i}-{\hat{f}}_{i}\left( \bar{ \eta }_{i}|\theta _{i}\right) \right) \nonumber \\= & {} Ae+\sum _{i=1}^{n}H_{i}\left( {\tilde{\theta }}_{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) +\varepsilon _{i}+\varDelta F_{i}\right) . \end{aligned}$$
(54)

Then, the time derivative of \(V_{e}\) can be obtained.

$$\begin{aligned} {\dot{V}}_{e}= & {} e^{\mathrm{T}}P\left( Ae+\sum _{i=1}^{n}H_{i}^{\mathrm{T}}{\tilde{\theta }} _{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) +\varDelta F_{i}+\varepsilon _{i}\right) \nonumber \\\le & {} -e^{\mathrm{T}}Qe+e^{\mathrm{T}}P\sum _{i=1}^{n}H_{i}^{\mathrm{T}}{\tilde{\theta }}_{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) \nonumber \\&+\,e^{\mathrm{T}}P\left( \varepsilon _{i}+\varDelta F_{i}\right) . \end{aligned}$$
(55)

Combining Young’s inequality and \(\varphi _{i}^{\mathrm{T}}\left( {\bar{\eta }} _{i}\right) \varphi _{i}\left( {\bar{\eta }}_{i}\right) \le 1\) yields

$$\begin{aligned} e^{\mathrm{T}}P\sum _{i=1}^{n}H_{i}{\tilde{\theta }}_{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }} _{i}\right) \!\le \! \frac{1}{2}\lambda _{\max }\left( P\right) \left| \left| e\right| \right| ^{2}+\sum _{j=1}^{n}{\tilde{\theta }}_{j}^{\mathrm{T}} {\tilde{\theta }}_{j}. \nonumber \\ \end{aligned}$$
(56)

By applying the method of Young’s inequality and Assumption 1, (58)–(57) holds.

$$\begin{aligned}&e^{\mathrm{T}}P\varDelta F_{i}\le \frac{1}{2}\left| \left| e\right| \right| ^{2}+\frac{1}{2}\sum _{j=1}^{n}\left| \left| P\right| \right| ^{2}m_{j}^{2}\left| \left| e\right| \right| ^{2}, \end{aligned}$$
(57)
$$\begin{aligned}&e^{\mathrm{T}}P\varepsilon _{i}\le \frac{1}{2}\left| \left| e\right| \right| ^{2}+\frac{1}{2}\sum _{j=1}^{n}\left| \left| P\right| \right| ^{2}\varepsilon _{j}^{*2}. \end{aligned}$$
(58)

Substituting (56)–(58) into (55) produces

$$\begin{aligned} {\dot{V}}_{e}\le & {} -\lambda _{\min }\left( Q\right) \left| \left| e\right| \right| ^{2}+\left( \frac{1}{2}\lambda _{\max }\left( P\right) +1\right) \left| \left| e\right| \right| ^{2} \nonumber \\&+\,\frac{1}{2}\left| \left| P\right| \right| ^{2}\sum \limits _{j=1}^{n}m_{j}^{2}\left| \left| e\right| \right| ^{2}+\sum _{j=1}^{n}\tilde{\theta }_{j}^{\mathrm{T}}\tilde{\theta }_{j} \nonumber \\&+\,\frac{1}{2}\sum _{j=1}^{n}\left| \left| P\right| \right| ^{2}\varepsilon _{j}^{*2} \nonumber \\\le & {} -q_{0}\left| \left| e\right| \right| ^{2}+\sum _{j=1}^{n}\tilde{\theta }_{j}^{\mathrm{T}}\tilde{\theta }_{j}+\lambda _{0}. \end{aligned}$$
(59)

Appendix B

The specific design process are given as follows:

Step 1:

Consider the following Lyapunov function candidate:

$$\begin{aligned} V_{z1}=\frac{1}{2}z_{1}^{2}+\frac{1}{2r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}\tilde{ \theta }_{1}. \end{aligned}$$
(60)

The time-derivative of \(V_{z1}\) can be expressed as

$$\begin{aligned} {\dot{V}}_{z1}= & {} z_{1}{\dot{z}}_{1}-\frac{1}{r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}\dot{ \theta }_{1} \nonumber \\= & {} z_{1}\left( \varGamma +\digamma \left( \eta _{2}+e_{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +{\tilde{\theta }}_{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \right) \right) \nonumber \\&+\,z_{1}\digamma \left( \varDelta F_{1}-{\dot{y}}_{d}+\varepsilon _{1}\right) - \frac{1}{r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}{\dot{\theta }}_{1} \nonumber \\= & {} z_{1}(\varGamma +\digamma \left( \alpha _{1}+e_{2}-{\dot{y}}_{d}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +\varepsilon _{1}\right) ) \nonumber \\&+\,\digamma z_{1}z_{2}+\frac{1}{r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}\left( r_{1}z_{1}\digamma \varphi _{1}\left( \eta _{1}\right) -{\dot{\theta }} _{1}\right) . \end{aligned}$$
(61)

By applying the method of Young’s inequality, it is easily verified that

$$\begin{aligned} z_{1}\digamma \left( e_{2}+\varDelta F_{1}\right)\le & {} z_{1}^{2}\digamma ^{2}+ \frac{1+m_{1}^{2}}{2}\left\| e\right\| ^{2}, \end{aligned}$$
(62)
$$\begin{aligned} z_{1}\digamma \varepsilon _{1}\le & {} \frac{1}{2}z_{1}^{2}\digamma ^{2}+ \frac{1}{2}\varepsilon _{1}^{*2}. \end{aligned}$$
(63)

Then, substituting (62) and (63) into (61) gives

$$\begin{aligned} {\dot{V}}_{z1}\le & {} z_{1}\left( \varGamma +\digamma \left( \alpha _{1}+\frac{3}{ 2}\digamma z_{1}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) -{\dot{y}} _{d}\right) \right) \nonumber \\&+\,\digamma z_{1}z_{2}+\frac{1}{2}\varepsilon _{1}^{*2}+\frac{1+m_{1}^{2} }{2}\left\| e\right\| ^{2} \nonumber \\&+\,\frac{1}{r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}\left( r_{1}z_{1}\digamma \varphi _{1}\left( \eta _{1}\right) -{\dot{\theta }}_{1}\right) . \end{aligned}$$
(64)

Substituting (38) and (39) into (64), it yields

$$\begin{aligned} {\dot{V}}_{z1}\le & {} -c_{1}z_{1}^{2}+\frac{\kappa _{1}}{r_{1}}{\tilde{\theta }} _{1}^{\mathrm{T}}\theta _{1}+\digamma z_{1}z_{2} \nonumber \\&+\,\frac{1+m_{1}^{2}}{2}\left\| e\right\| ^{2}+\frac{1}{2}\varepsilon _{1}^{*2}. \end{aligned}$$
(65)

Based on Young’s inequality, it produces that

$$\begin{aligned} {\tilde{\theta }}_{1}^{\mathrm{T}}\theta _{1}\le \frac{1}{2}\left| \left| \theta _{1}^{*}\right| \right| ^{2}-\frac{1}{2}{\tilde{\theta }} _{1}^{\mathrm{T}}{\tilde{\theta }}_{1}. \end{aligned}$$
(66)

Therefore, (64) can be rewritten as

$$\begin{aligned} {\dot{V}}_{z1}\le & {} -c_{1}z_{1}^{2}-\frac{\kappa _{1}}{2r_{1}}{\tilde{\theta }} _{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\digamma z_{1}z_{2}+\frac{1+m_{1}^{2}}{2} \left\| e\right\| ^{2} \nonumber \\&+\,\frac{\kappa _{1}}{2r_{1}}\left| \left| \theta _{1}^{*}\right| \right| ^{2}+\frac{1}{2}\varepsilon _{1}^{*2}. \end{aligned}$$
(67)

Step 2:

Construct the Lyapunov function for the second subsystem as follows:

$$\begin{aligned} V_{z2}=V_{z1}+\frac{1}{2}z_{2}^{2}+\frac{1}{2r_{2}}{\tilde{\theta }}_{2}^{\mathrm{T}} {\tilde{\theta }}_{2}. \end{aligned}$$
(68)

The time-derivative of \(V_{z2}\) can be described as

$$\begin{aligned} {\dot{V}}_{z2}= & {} {\dot{V}}_{z1}+z_{2}{\dot{z}}_{2}-\frac{1}{r_{2}}{\tilde{\theta }} _{2}^{\mathrm{T}}{\dot{\theta }}_{2} \nonumber \\= & {} {\dot{V}}_{z1}+z_{2}\left( \eta _{3}+\theta _{2}^{\mathrm{T}}\varphi _{2}\left( \bar{ \eta }_{2}\right) +k_{2}e_{1}\right) \nonumber \\&-\,z_{2}{\dot{\alpha }}_{1}-\frac{1}{r_{2}}{\tilde{\theta }}_{2}^{\mathrm{T}}{\dot{\theta }} _{2}, \end{aligned}$$
(69)

where

$$\begin{aligned} {\dot{\alpha }}_{1}= & {} \frac{\partial \alpha _{1}}{\partial y}\left( \eta _{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +{\tilde{\theta }} _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +e_{2}+\varepsilon _{1}\right) \nonumber \\&+\,\frac{\partial \alpha _{1}}{\partial \eta _{1}}\left( \eta _{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +k_{1}e_{1}\right) \nonumber \\&+\,\sum _{j=1}^{2}\frac{\partial \alpha _{1}}{\partial y_{d}^{\left( j-1\right) }}y_{d}^{\left( j\right) }+\frac{\partial \alpha _{1}}{\partial \theta _{1}}{\dot{\theta }}_{1}+\frac{\partial \alpha _{1}}{\partial \nu }\nu ^{\left( 1\right) }. \end{aligned}$$
(70)

According to (70) , \({\dot{V}}_{z2}\) can be rewritten as

$$\begin{aligned} {\dot{V}}_{z2}= & {} {\dot{V}}_{z1}+z_{2}\left( z_{3}+\alpha _{2}+k_{2}e_{1}+\theta _{2}^{\mathrm{T}}\varphi _{2}\left( {\bar{\eta }}_{2}\right) \right) \nonumber \\&+\,z_{2}\left( {\tilde{\theta }}_{2}^{\mathrm{T}}\varphi _{2}\left( {\bar{\eta }} _{2}\right) -{\tilde{\theta }}_{2}^{\mathrm{T}}\varphi _{2}\left( {\bar{\eta }}_{2}\right) - \frac{\partial \alpha _{1}}{\partial y}\left( \varepsilon _{1}+e_{2}\right) \right) \nonumber \\&-\,z_{2}\left( \frac{\partial \alpha _{1}}{\partial y}({\tilde{\theta }} _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +\eta _{2})\right) \nonumber \\&-\,z_{2}\frac{\partial \alpha _{1}}{\partial \eta _{1}}\left( \eta _{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +k_{1}e_{1}\right) \nonumber \\&-\,z_{2}\left( \sum _{j=1}^{2}\frac{\partial \alpha _{1}}{\partial y_{d}^{\left( j-1\right) }}y_{d}^{\left( j\right) }+\frac{\partial \alpha _{1}}{\partial \theta _{1}}{\dot{\theta }}_{1}\right) \nonumber \\&-\,z_{2}\frac{\partial \alpha _{1}}{\partial \nu }\nu ^{\left( 1\right) }- \frac{1}{r_{2}}{\tilde{\theta }}_{2}^{\mathrm{T}}{\dot{\theta }}_{2}. \end{aligned}$$
(71)

By applying the method of Young’s inequality, it is verified that

$$\begin{aligned} -z_{2}{\tilde{\theta }}_{2}^{\mathrm{T}}\varphi _{2}\left( {\bar{\eta }}_{2}\right) \le \frac{1}{2}z_{2}^{2}+\frac{1}{2}{\tilde{\theta }}_{2}^{\mathrm{T}}{\tilde{\theta }}_{2}, \end{aligned}$$
(72)
$$\begin{aligned} -z_{2}\frac{\partial \alpha _{1}}{\partial y}{\tilde{\theta }}_{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \le \frac{1}{2}z_{2}^{2}\left( \frac{\partial \alpha _{1}}{\partial y}\right) ^{2}+\frac{1}{2}{\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}, \end{aligned}$$
(73)
$$\begin{aligned} -z_{2}\frac{\partial \alpha _{1}}{\partial y}\left( \varepsilon _{1}+e_{2}\right) \le z_{2}^{2}\left( \frac{\partial \alpha _{1}}{\partial y }\right) ^{2}\!+\!\frac{1}{2}\varepsilon _{1}^{*2}\!+\!\frac{1}{2}\left\| e\right\| ^{2}. \nonumber \\ \end{aligned}$$
(74)

Then, substituting (67), (72)–(74) into (71) yields

$$\begin{aligned} {\dot{V}}_{z2}\le & {} -c_{1}z_{1}^{2}+z_{2}\left( z_{3}+\alpha _{2}+D_{2}+\digamma z_{1}+\frac{1}{2}z_{2}\right) \nonumber \\&+\,\frac{3}{2}z_{2}^{2}\left( \frac{\partial \alpha _{1}}{\partial y}\right) ^{2}+\frac{1}{r_{2}}{\tilde{\theta }}_{2}^{\mathrm{T}}\left( r_{2}z_{2}\varphi _{2}\left( {\bar{\eta }}_{2}\right) -{\dot{\theta }}_{2}\right) \nonumber \\&+\,\sum _{j=1}^{2}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }}_{j}+\frac{ \kappa _{1}}{2r_{1}}\left| \left| \theta _{1}^{*}\right| \right| ^{2} \nonumber \\&-\,\frac{\kappa _{1}}{2r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }} _{1}+\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}+\frac{1}{2} m_{1}^{2}\left\| e\right\| ^{2}. \end{aligned}$$
(75)

According the design of controller, substituting (38) and (39) into (40) yields

$$\begin{aligned} {\dot{V}}_{z2}\le & {} -\sum _{j=1}^{2}c_{j}z_{j}^{2}+z_{2}z_{3}+\frac{\kappa _{2}}{r_{2}}{\tilde{\theta }}_{2}^{\mathrm{T}}\theta _{2}+\sum _{j=1}^{2}\frac{1}{2} {\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }}_{j} \nonumber \\&-\,\frac{\kappa _{1}}{2r_{1}}{\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\frac{ \kappa _{1}}{2r_{1}}\left| \left| \theta _{1}^{*}\right| \right| ^{2} \nonumber \\&+\,\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}+\frac{1}{2} m_{1}^{2}\left\| e\right\| ^{2} \nonumber \\\le & {} -\sum _{j=1}^{2}c_{j}z_{j}^{2}+z_{2}z_{3}-\sum _{j=1}^{2}\frac{\kappa _{j}}{2r_{j}}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }}_{j} \nonumber \\&+\,\sum _{j=1}^{2}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }} _{j}+\sum _{j=1}^{2}\frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2} \nonumber \\&+\,\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}+\frac{1}{2} m_{1}^{2}\left\| e\right\| ^{2}. \end{aligned}$$
(76)

Step i:

Construct the Lyapunov function for the i-th subsystem as follows:

$$\begin{aligned} V_{zi}=V_{zi-1}+\frac{1}{2}z_{i}^{2}+\frac{1}{2r_{i}}{\tilde{\theta }}_{i}^{\mathrm{T}} {\tilde{\theta }}_{i}. \end{aligned}$$
(77)

Subsequently, the time-derivative of \(V_{zi}\) can be described as

$$\begin{aligned} {\dot{V}}_{zi}= & {} {\dot{V}}_{zi-1}+z_{i}\left( \eta _{i+1}+\theta _{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) +k_{i}e_{1}\right) \nonumber \\&-\,z_{i}{\dot{\alpha }}_{i-1}-\frac{1}{r_{i}}{\tilde{\theta }}_{i}^{\mathrm{T}}{\dot{\theta }} _{i}, \end{aligned}$$
(78)

where

$$\begin{aligned} {\dot{\alpha }}_{i-1}= & {} \frac{\partial \alpha _{i-1}}{\partial y}\left( \eta _{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +{\tilde{\theta }} _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +e_{2}+\varepsilon _{1}\right) \nonumber \\&+\,\sum _{j=1}^{i}\frac{\partial \alpha _{j-1}}{\partial y_{d}^{\left( j-1\right) }}y_{d}^{\left( j\right) }+\sum _{j=1}^{i-1}\frac{\partial \alpha _{j}}{\partial \theta _{j}}\theta _{j}^{\left( j\right) } \nonumber \\&+\,\sum _{j=1}^{i-1}\frac{\partial \alpha _{j}}{\partial \eta _{j}}\left( \eta _{j+1}+\theta _{j}^{\mathrm{T}}\varphi _{j}\left( {\bar{\eta }}_{j}\right) +k_{j}e_{1}\right) \nonumber \\&+\,\sum _{j=1}^{i-1}\frac{\partial \alpha _{j-1}}{\partial \nu ^{\left( j-1\right) }}\nu ^{\left( j\right) }. \end{aligned}$$
(79)

According to (79) , \({\dot{V}}_{zi}\) can be expressed as

$$\begin{aligned} {\dot{V}}_{zi}= & {} {\dot{V}}_{zi-1}+z_{i}\left( z_{i+1}+\alpha _{i}+k_{i}e_{1}+\theta _{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) \right) \nonumber \\&+\,z_{i}\left( {\tilde{\theta }}_{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }} _{i}\right) -{\tilde{\theta }}_{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) - \frac{\partial \alpha _{i-1}}{\partial y}{\tilde{\theta }}_{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \right) \nonumber \\&-\,z_{i}\left( \frac{\partial \alpha _{i-1}}{\partial y}\left( e_{2}+\varepsilon _{1}+\eta _{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \right) \right) \nonumber \\&-\,z_{i}\left( \sum _{j=1}^{i-1}\frac{\partial \alpha _{j}}{\partial \eta _{j} }\left( \eta _{j+1}+\theta _{j}^{\mathrm{T}}\varphi _{j}\left( {\bar{\eta }}_{j}\right) +k_{j}e_{1}\right) \right) \nonumber \\&-\,z_{i}\left( \sum _{j=1}^{i-1}\frac{\partial \alpha _{j}}{\partial \theta _{j}}\theta _{j}^{\left( j\right) }+\sum _{j=1}^{i}\frac{\partial \alpha _{j-1}}{\partial y_{d}^{\left( j-1\right) }}y_{d}^{\left( j\right) }\right) \nonumber \\&-\,z_{i}\sum _{j=1}^{i-1}\frac{\partial \alpha _{j-1}}{\partial \nu ^{\left( j-1\right) }}\nu ^{\left( j\right) }-\frac{1}{r_{i}}{\tilde{\theta }}_{i}^{\mathrm{T}} {\dot{\theta }}_{i}. \end{aligned}$$
(80)

By applying the method of Young’s inequality, it is verified that

$$\begin{aligned}&-\,z_{i}{\tilde{\theta }}_{i}^{\mathrm{T}}\varphi _{i}\left( {\bar{\eta }}_{i}\right) \le \frac{1}{2}z_{i}^{2}+\frac{1}{2}{\tilde{\theta }}_{i}^{\mathrm{T}}{\tilde{\theta }}_{i}, \end{aligned}$$
(81)
$$\begin{aligned}&-\,z_{i}\frac{\partial \alpha _{i-1}}{\partial y}{\tilde{\theta }}_{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \le \frac{1}{2}z_{i}^{2}\left( \frac{\partial \alpha _{i-1}}{\partial y}\right) ^{2}+\frac{1}{2}{\tilde{\theta }}_{1}^{\mathrm{T}} {\tilde{\theta }}_{1}, \end{aligned}$$
(82)
$$\begin{aligned}&-\,z_{i}\frac{\partial \alpha _{i-1}}{\partial y}e_{2}\le \frac{1}{2} z_{i}^{2}\left( \frac{\partial \alpha _{i-1}}{\partial y}\right) ^{2}+\frac{1 }{2}\left\| e\right\| ^{2}, \end{aligned}$$
(83)
$$\begin{aligned}&-\,z_{i}\frac{\partial \alpha _{i-1}}{\partial y}\varepsilon _{1}\le \frac{1}{ 2}z_{i}^{2}\left( \frac{\partial \alpha _{i-1}}{\partial y}\right) ^{2}+ \frac{1}{2}\varepsilon _{1}^{*2}. \end{aligned}$$
(84)

Then, substituting (81)–(84) into (80) yields

$$\begin{aligned} {\dot{V}}_{zi}\le & {} -\sum _{j=1}^{i-1}c_{j}z_{j}^{2}+z_{i}\left( z_{i+1}+\alpha _{i}+D_{i}+\frac{1}{2}z_{i}\right) \nonumber \\&+\,\frac{3}{2}z_{i}^{2}\left( \frac{\partial \alpha _{i-1}}{\partial y} \right) ^{2}+\frac{1}{r_{i}}{\tilde{\theta }}_{i}^{\mathrm{T}}\left( r_{i}z_{i}\varphi _{i}\left( {\bar{\eta }}_{i}\right) -{\dot{\theta }}_{i}\right) \nonumber \\&-\,\sum _{j=1}^{i-1}\frac{\kappa _{j}}{2r_{j}}{\tilde{\theta }}_{j}^{\mathrm{T}}\tilde{ \theta }_{j}+\sum _{j=2}^{i}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }} _{j}+\sum _{j=1}^{i-1}\frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2} \nonumber \\&+\,\frac{i}{2}\left( {\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}\right) +\frac{1}{2} m_{1}^{2}\left\| e\right\| ^{2}. \end{aligned}$$
(85)

Substituting (38) and (39) into (40 , \({\dot{V}}_{zi}\) is expressed as

$$\begin{aligned} {\dot{V}}_{zi}\le & {} -\sum _{j=1}^{i}c_{j}z_{j}^{2}+z_{i}z_{i+1}+\frac{i}{2} \left( {\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}\right) \nonumber \\&-\,\sum _{j=1}^{i}\frac{\kappa _{j}}{2r_{j}}{\tilde{\theta }}_{j}^{\mathrm{T}}\tilde{ \theta }_{j}+\sum _{j=1}^{i}\frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2} \nonumber \\&+\,\sum _{j=2}^{i}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }}_{j}+\frac{1 }{2}m_{1}^{2}\left\| e\right\| ^{2}. \end{aligned}$$
(86)

Step n:

Differentiating \(z_{n}=\eta _{n}-\alpha _{n-1}\) with respect to time yields

$$\begin{aligned} {\dot{z}}_{n}= & {} u+\theta _{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }}_{n}\right) +k_{n}e_{1}-{\dot{\alpha }}_{n-1} \nonumber \\= & {} h\left( v\right) +d\left( v\right) +\theta _{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }}_{n}\right) +k_{n}e_{1}-{\dot{\alpha }}_{n-1} \end{aligned}$$
(87)

where

$$\begin{aligned} {\dot{\alpha }}_{n-1}= & {} \sum _{j=1}^{n-1}\frac{\partial \alpha _{j}}{\partial \eta _{j}}\left( \eta _{j+1}+\theta _{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }} _{n}\right) +k_{j}e_{1}\right) \nonumber \\&+\,\frac{\partial \alpha _{n-1}}{\partial y}\left( \eta _{2}+\theta _{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }}_{n}\right) +{\tilde{\theta }} _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) +e_{2}\right) \nonumber \\&+\,\frac{\partial \alpha _{n-1}}{\partial y}\varepsilon _{1}+\sum _{j=1}^{n} \frac{\partial \alpha _{j-1}}{\partial y_{d}^{\left( j-1\right) }} y_{d}^{\left( j\right) } \nonumber \\&+\,\sum _{j=1}^{n-1}\frac{\partial \alpha _{j}}{\partial \theta _{j}}\theta _{j}^{\left( j\right) }+\sum _{j=1}^{n-1}\frac{\partial \alpha _{j-1}}{ \partial \nu ^{\left( j-1\right) }}\nu ^{\left( j\right) }. \end{aligned}$$
(88)

Construct the Lyapunov function for the n-th subsystem as follows:

$$\begin{aligned} V_{zn}=V_{zn-1}+\frac{1}{2}z_{n}^{2}+\frac{1}{2r_{n}}{\tilde{\theta }}_{n}^{\mathrm{T}} {\tilde{\theta }}_{n}. \end{aligned}$$
(89)

From (87), the time-derivative of \(V_{zn}\) can be described as

$$\begin{aligned} {\dot{V}}_{zn}= & {} {\dot{V}}_{zn-1}+z_{n}{\dot{z}}_{n}-\frac{1}{r_{n}}{\tilde{\theta }} _{n}^{\mathrm{T}}{\dot{\theta }}_{n} \nonumber \\= & {} {\dot{V}}_{zn-1}+z_{n}\left( d\left( v\right) +h\left( v\right) +\theta _{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }}_{n}\right) +k_{n}e_{1}\right) \nonumber \\&-\,z_{n}{\dot{\alpha }}_{n-1}-\frac{1}{r_{n}}{\tilde{\theta }}_{n}^{\mathrm{T}}{\dot{\theta }} _{n}. \end{aligned}$$
(90)

Then, substituting (13) into (90), \({\dot{V}}_{zn}\) can be rewritten as

$$\begin{aligned} {\dot{V}}_{zn}= & {} {\dot{V}}_{zn-1}+z_{n}\left( {\dot{d}}\left( v_{u}\right) v+h\left( v\right) +k_{n}e_{1}+\theta _{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }} _{n}\right) \right) \nonumber \\&+\,z_{n}\left( {\tilde{\theta }}_{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }} _{n}\right) -\sum _{j=1}^{n-1}\frac{\partial \alpha _{j}}{\partial \eta _{j}} \left( \eta _{j+1}+k_{j}e_{1}\right) \right) \nonumber \\&-\,z_{n}\left( \frac{\partial \alpha _{n-1}}{\partial y}\left( e_{2}+\varepsilon _{1}+{\tilde{\theta }}_{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \right) \right) \nonumber \\&-\,z_{n}\left( \frac{\partial \alpha _{n-1}}{\partial y}\left( \eta _{2}+\theta _{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \right) \right) \nonumber \\&-\,z_{n}\left( \sum _{j=1}^{n-1}\frac{\partial \alpha _{j}}{\partial \eta _{j} }\theta _{j}^{\mathrm{T}}\varphi _{j}\left( {\bar{\eta }}_{j}\right) +\sum _{j=1}^{n} \frac{\partial \alpha _{j-1}}{\partial y_{d}^{\left( j-1\right) }} y_{d}^{\left( j\right) }\right) \nonumber \\&-\,z_{n}\left( \sum _{j=1}^{n-1}\frac{\partial \alpha _{j-1}}{\partial \nu ^{\left( j-1\right) }}\nu ^{\left( j\right) }+\sum _{j=1}^{n-1}\frac{\partial \alpha _{j}}{\partial \theta _{j}}\theta _{j}^{\left( j\right) }\right) \nonumber \\&+\,\frac{1}{r_{n}}{\tilde{\theta }}_{n}^{\mathrm{T}}\left( r_{n}z_{n}\varphi _{n}\left( {\bar{\eta }}_{n}\right) -{\dot{\theta }}_{n}\right) . \end{aligned}$$
(91)

By applying the method of Young’s inequality, the following results hold.

$$\begin{aligned}&-\,z_{n}{\tilde{\theta }}_{n}^{\mathrm{T}}\varphi _{n}\left( {\bar{\eta }}_{n}\right) \le \frac{1}{2}z_{n}^{2}+\frac{1}{2}{\tilde{\theta }}_{n}^{\mathrm{T}}{\tilde{\theta }}_{n}, \end{aligned}$$
(92)
$$\begin{aligned}&-\,z_{n}\frac{\partial \alpha _{n-1}}{\partial y}{\tilde{\theta }}_{1}^{\mathrm{T}}\varphi _{1}\left( \eta _{1}\right) \le \frac{1}{2}z_{n}^{2}\left( \frac{\partial \alpha _{n-1}}{\partial y}\right) ^{2} \nonumber \\&\quad +\frac{1}{2}{\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}, \end{aligned}$$
(93)
$$\begin{aligned}&-\,z_{n}\frac{\partial \alpha _{n-1}}{\partial y}\left( \varepsilon _{1}+e_{2}\right) \le z_{n}^{2}\left( \frac{\partial \alpha _{n-1}}{ \partial y}\right) ^{2}+\frac{1}{2}\varepsilon _{1}^{*2} \nonumber \\&\quad +\frac{1}{2}\left\| e\right\| ^{2}. \end{aligned}$$
(94)

Then, substituting (92)–(94) into (91) yields

$$\begin{aligned} {\dot{V}}_{zn}\le & {} -\sum _{j=1}^{n-1}c_{j}z_{j}^{2}+z_{n}\left( {\dot{d}} \left( v_{u}\right) v+h\left( v\right) +D_{n}+\frac{1}{2}z_{n}\right) \nonumber \\&+\,\frac{3}{2}z_{n}^{2}\left( \frac{\partial \alpha _{n-1}}{\partial y} \right) ^{2}+\frac{1}{r_{n}}{\tilde{\theta }}_{n}^{\mathrm{T}}\left( r_{n}z_{n}\varphi _{n}\left( {\bar{\eta }}_{n}\right) -{\dot{\theta }}_{n}\right) \nonumber \\&-\,\sum _{j=1}^{n-1}\frac{\kappa _{j}}{2r_{j}}{\tilde{\theta }}_{j}^{\mathrm{T}}\tilde{ \theta }_{j}+\sum _{j=2}^{n}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }} _{j}+\sum _{j=1}^{n-1}\frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2} \nonumber \\&+\,\frac{n}{2}\left( {\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}\right) +\frac{1}{2} m_{1}^{2}\left\| e\right\| ^{2}. \end{aligned}$$
(95)

Based on Assumption 4 and Young’s inequality, the following inequality can be obtained.

$$\begin{aligned} z_{n}h\left( v\right) \le \frac{1}{2}z_{n}^{2}+\frac{1}{2}q^{2}. \end{aligned}$$
(96)

Note that in (95), \({\dot{d}}\left( v_{u}\right) >0\) satisfies Assumption 5, it is similar to the processing method in [38]. Therefore, consider the control laws (41) and adaptive law (39), (95) can be rewritten as

$$\begin{aligned} {\dot{V}}_{zn}\le & {} -\sum _{j=1}^{n-1}c_{j}z_{j}^{2}+z_{n}\left( {\dot{d}} \left( v_{u}\right) N\left( \xi \right) {\bar{v}}\left( t\right) +D_{n}+z_{n}\right) \nonumber \\&+\,\frac{3}{2}z_{n}^{2}\left( \frac{\partial \alpha _{n-1}}{\partial y} \right) ^{2}+\sum _{j=1}^{n}\frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2} \nonumber \\&-\,\sum _{j=1}^{n}\frac{\kappa _{j}}{2r_{j}}{\tilde{\theta }}_{j}^{\mathrm{T}}\tilde{ \theta }_{j}+\sum _{j=2}^{n}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }}_{j} \nonumber \\&+\,\frac{n}{2}\left( {\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}\right) +\frac{1}{2} m_{1}^{2}\left\| e\right\| ^{2}+\frac{1}{2}q^{2} \nonumber \\= & {} \left( {\dot{d}}\left( v_{u}\right) N\left( \xi \right) +1\right) {\dot{\xi }} -\sum _{j=1}^{n}c_{j}z_{j}^{2}-\sum _{j=1}^{n}\frac{\kappa _{j}}{2r_{j}}\tilde{ \theta }_{j}^{\mathrm{T}}{\tilde{\theta }}_{j} \nonumber \\&+\,\sum _{j=2}^{n}\frac{1}{2}{\tilde{\theta }}_{j}^{\mathrm{T}}{\tilde{\theta }}_{j}+\frac{n }{2}\left( {\tilde{\theta }}_{1}^{\mathrm{T}}{\tilde{\theta }}_{1}+\left\| e\right\| ^{2}+\varepsilon _{1}^{*2}\right) \nonumber \\&+\,\sum _{j=1}^{n}\frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2}+\frac{1}{2}m_{1}^{2}\left\| e\right\| ^{2}+\frac{1}{2 }q^{2}. \end{aligned}$$
(97)

Consider the Lyapunov function candidate as \(V=V_{e}+V_{zn}\), and the time-derivative of V can be described as

$$\begin{aligned} {\dot{V}}= & {} {\dot{V}}_{e}+{\dot{V}}_{zn} \nonumber \\\le & {} -aV+\left( {\dot{d}}\left( v_{u}\right) N\left( \xi \right) +1\right) {\dot{\xi }}+b_{0}. \end{aligned}$$
(98)

where \(a=\min \left\{ \frac{2a_{0}}{\lambda _{\max }\left( P\right) } ,2c_{j},2\beta r_{j},\kappa _{j}\right\} \) \(\left( j=1,\cdots ,n\right) \) with \(a_{0}=q_{0}-\frac{n}{2}-\frac{1}{2}m_{1}^{2}\), \(\beta =\frac{\kappa _{j}}{2r_{j}}-\frac{n+1}{2}\) and \(b_{0}=\lambda _{0}+\sum \limits _{j=1}^{n} \frac{\kappa _{j}}{2r_{j}}\left\| \theta _{j}^{*}\right\| ^{2}+ \frac{1}{2}q^{2}+\frac{n}{2}\varepsilon _{1}^{*2}\).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, X., Gao, C., Li, Zg. et al. Observer-based adaptive fuzzy finite-time prescribed performance tracking control for strict-feedback systems with input dead-zone and saturation. Nonlinear Dyn 103, 1645–1661 (2021). https://doi.org/10.1007/s11071-020-06190-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-020-06190-5

Keywords

Navigation