Skip to main content
Log in

Nonlinear adaptive control for an unmanned aerial payload transportation system: theory and experimental validation

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

Abstract

In this paper, the position control and swing motion control problem are investigated for an aerial payload transportation system which consists of a quadrotor unmanned aerial vehicle (UAV) and a suspended payload. Under the constraints of underactuated properties and unknown system parameters, a nonlinear adaptive control strategy is designed based on the energy methodology, which achieves accurate position control of the UAV as well as the payload’s fast swing suppression during the flight. The stability of the closed-loop system, asymptotic convergence of the UAV’s position error and payload swing suppression are proved via Lyapunov-based stability analysis. Real-time experimental results validate the effectiveness of the developed technique.

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

Similar content being viewed by others

References

  1. Zhao, B., Xian, B., Zhang, Y., Zhang, X.: Nonlinear robust adaptive tracking control of a quadrotor UAV via immersion and invariance methodology. IEEE Trans. Ind. Electron. 62(5), 2891–2902 (2015)

    Article  Google Scholar 

  2. Teuliere, C., Marchand, E., Eck, L.: 3-D model-based tracking for UAV indoor localization. IEEE Trans. Cybern. 45(5), 869–879 (2015)

    Article  Google Scholar 

  3. Xie, H., Low, K.H., He, Z.: Adaptive visual servoing of unmanned aerial vehicles in GPS-denied environments. IEEE-ASME Trans. Mechatron. 22(6), 2554–2563 (2017)

    Article  Google Scholar 

  4. Xian, B., Hao, W.: Nonlinear robust fault-tolerant control of the tilt trirotor UAV under rear servo’s stuck fault: theory and experiments. IEEE Trans. Ind. Inf. 15(4), 2158–2166 (2019)

    Article  Google Scholar 

  5. Xian, B., Diao, C., Zhao, B., Zhang, Y.: Nonlinear robust output feedback tracking control of a quadrotor UAV using quaternion representation. Nonlinear Dyn. 79(4), 2735–2752 (2015)

    Article  MathSciNet  Google Scholar 

  6. Xian, B., Zhao, B., Zhang, Y., Zhang, X.: Nonlinear control of a quadrotor with deviated center of gravity. J. Dyn. Syst. Meas. Control-Trans. ASME 139(1), 011003-1–011003-7 (2017)

  7. Tomic, T., Schmid, K., Lutz, P.: Toward a fully autonomous UAV: research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)

    Article  Google Scholar 

  8. Mellinger, D., Michael, N., Kumar, V.: Trajectory generation and control for precise aggressive maneuvers with quadrotors. Int. J. Robot. Res. 31(5), 664–674 (2012)

    Article  Google Scholar 

  9. Ghamry, K.A., Zhang, Y.: Fault-tolerant cooperative control of multiple UAVs for forest fire detection and tracking mission. In: Proceeding of the 3rd Control and Fault-Tolerant Systems, Barcelona, Spain, Sept. 07–09, pp. 133–138 (2016)

  10. Alvear, O., Zema, N.R., Natalizio, E., Calafate, C.T.: Using UAV-based systems to monitor air pollution in areas with poor accessibility. J. Adv. Transp., to be published. https://doi.org/10.1155/2017/8204353

    Article  Google Scholar 

  11. Jia, B., Chen, J., Zhang, K.: Drivable road reconstruction for intelligent vehicles based on two-view geometry. IEEE Trans. Ind. Electron 64(5), 3696–3706 (2017)

    Article  Google Scholar 

  12. Cao, L., Qiao, D., Chen, X.: Laplace L1 Huber based cubature Kalman filter for attitude estimation of small satellite. Acta Astronaut. 148, 48–56 (2018)

    Article  Google Scholar 

  13. Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19(3), 20–32 (2012)

    Article  Google Scholar 

  14. Xiao, B., Yang, X., Karimi, H.R., Qiu, J.: Asymptotic tracking control for a more representative class of uncertain nonlinear systems with mismatched uncertainties. IEEE Trans. Ind. Electron. to be published. https://doi.org/10.1109/TIE.2019.2893852

    Article  Google Scholar 

  15. Xiao, B., Yin, S.: Exponential tracking control of robotic manipulators with uncertain dynamics and kinematics. IEEE Trans. Ind. Inf. 15(2), 689–698 (2019)

    Article  Google Scholar 

  16. L’Afflitto, A., Anderson, R.B., Mohammadi, K.: An introduction to nonlinear robust control for unmanned quadrotor aircraft: how to design control algorithms for quadrotors using sliding mode control and adaptive control techniques. IEEE Control Syst. Mag. 38(3), 102–121 (2018)

    Article  MathSciNet  Google Scholar 

  17. Liu, H., Ma, T., Lewis, F.L., Wan, Y.: Robust formation control for multiple quadrotors with nonlinearities and disturbances. IEEE Trans. Cybern., pp. 1–10. https://doi.org/10.1109/TCYB.2018.2875559

  18. Liu, H., Li, D., Zuo, Z., Zhong, Y.: Robust three-loop trajectory tracking control for quadrotors with multiple uncertainties. IEEE Trans. Ind. Electron. 63(4), 2263–2274 (2016)

    Google Scholar 

  19. Tang, S., Kumar, V.: Mixed integer quadratic program trajectory generation for a quadrotor with a cable-suspended payload. In: Proceedings of the IEEE Conference on Robotics and Automation, Seattle, WA, USA, May 26–30, pp. 2216–2222 (2015)

  20. Faust, A., Palunko, I., Cruz, P., Fierro, R., Tapia, L.: Learning swing-free trajectories for UAVs with a suspended load. Proceedings of the IEEE Conference on Robotics and Automation, Karlsruhe, Germany, May. 06–10, 4902–4909 (2013)

    Google Scholar 

  21. Cruz, P.J., Fierro, R.: Cable-suspended load lifting by a quadrotor UAV: hybrid model, trajectory generation, and control. Auton. Robot. 41(8), 1629–1643 (2017)

    Article  Google Scholar 

  22. Cruz, P.J., Oishi, M., Fierro, R.: Lift of a cable-suspended load by a quadrotor: a hybrid system approach. Proceedings of the American Control Conference, Chicago, IL, USA, Jan. 01–03, 1887–1892 (2015)

    Google Scholar 

  23. Goodarzi, F.A., Lee, D., Lee, T.: Geometric control of a quadrotor UAV transporting a payload connected via flexible cable. Int. J. Control Autom. Syst. 13(6), 1486–1498 (2015)

    Article  Google Scholar 

  24. Dai, S., Lee, T., Bernstein, D.S.: Adaptive control of a quadrotor UAV transporting a cable-suspended load with unknown mass. In: Proceedings of the 53rd IEEE Conference on Decision Control, Los Angeles, CA, USA, Dec. 15–17, pp. 6149–6154 (2014)

  25. Wu, G., Sreenath, K.: Geometric control of multiple quadrotors transporting a rigid-body load. In: Proceedings of the 53rd IEEE Conference on Decision Control, Los Angeles, CA, USA, Dec. 15–17, pp. 6141–6148 (2014)

  26. Lee, T., Sreenath, K., Kumar, V.: Geometric control of cooperating multiple quadrotor UAVs with a suspended payload. In: Proceedings of the 52nd IEEE Conference on Decision Control, Florence, Italy, Dec. 10–13, pp. 5510–5515 (2013)

  27. Lee, T.: Geometric control of multiple quadrotor UAVs transporting a cable-suspended rigid body. In: Proceedings of the 53rd IEEE Conference on Decision Control, Los Angeles, CA, USA, Dec. 15–17, pp. 6155–6160 (2014)

  28. Palunko, I., Fierro, R., Cruz, P.: Trajectory generation for swing-free maneuvers of a quadrotor with suspended payload: a dynamic programming approach. In: Proceedings of the IEEE Conference on Robotics and Automation, St Paul, MN, USA, May 14–18, pp. 2691–2697 (2012)

  29. Sreenath, K., Michael, N., Kumar, V.: Trajectory generation and control of a quadrotor with a cable-suspended load–a differentially-flat hybrid system. In: Proceedings of the IEEE Conference on Robotics and Automation, Karlsruhe, Germany, May 06–11, pp. 4888–4895 (2013)

  30. Palunko, I., Faust, A., Cruz, P., Tapia, L., Fierro, R.: A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots. In: Proceedings of the IEEE Conference on Robotics and Automation, Karlsruhe, Germany, May 06–11, pp. 4881–4886 (2013)

  31. Liang, X., Fang, Y., Sun, N.: Nonlinear hierarchical control for unmanned quadrotor transportation systems. IEEE Trans. Ind. Electron. 65(4), 3395–3405 (2018)

    Article  Google Scholar 

  32. Alothman, Y., Jasim, W., Gu, D.: Quad-rotor lifting-transporting cable-suspended payloads control. In: Proceedings of the 21st International Conference on Automation and Computing, Glasgow, UK, Sept. 11–12, pp. 1–6 (2015)

  33. Alothman, Y., Gu, D.: Quadrotor transporting cable-suspended load using iterative linear quadratic regulator (iLQR) optimal control. In: Proceedings of the 8th Computer Science and Electronic Engineering, Colchester, UK, Sept. 28–30, pp. 168–173 (2016)

  34. Yang, S., Xian, B.: Energy-based nonlinear adaptive control design for the quadrotor UAV system with a suspended payload. IEEE Trans. Ind. Electron., In Press (2019). https://doi.org/10.1109/TIE.2019.2902834

    Article  Google Scholar 

  35. Wu, C., Fu, Z., Yang, J., Wei, Y.: Nonlinear control and analysis of a quadrotor with sling load in path tracking. In: Proceedings of the 36th Chinese Control Conference, DaLian, China, Jul. 10–13, pp. 6519–652 (2017)

  36. Klausen, K., Fossen, T.I., Johansenn, T.A.: Nonlinear control of a multirotor UAV with suspended load. In: Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, CO, USA, June 9–12, pp. 176–184 (2015)

  37. Liang, X., Fang, Y., Sun, N.: A hierarchical controller for quadrotor unmanned aerial vehicle transportation systems. In: Proceedings of the 35th Chinese Control Conference, Chengdu, China, Jul. 27–29, pp. 6148–6153 (2016)

  38. Nicotra, M.M., Garone, E., Naldi, R., Marconi, L.: Nested saturation control of an UAV carrying a suspended load. In: Proceedings of the American Control Conference, Portland, OR, USA, June 4–6, pp. 3585–3590 (2014)

  39. Sun, N., Yang, T., Fang, Y., Wu, Y., Chen, H.: Transportation control of double-pendulum cranes with a nonlinear quasi-pid scheme: design and experiments. IEEE Trans. Syst Man Cybern. Syst. 49(7), 1408–1418 (2019)

    Article  Google Scholar 

  40. Khalil, H.K.: Nonlinear Systems, 3rd edn. Prentice-Hall, Englewood Cliffs, NJ (2002)

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key R & D Program of China (2018YFB1403900) and the National Natural Science Foundation of China (91748121, 90916004).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bin Xian.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human participants

The research work of this submission does not involve any human participants. The research work of this submission does not involve any animals.

Additional information

Publisher's Note

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

Appendix

Appendix

1.1 Parameter condition derivation

The function \(\varLambda (t)\) in (48) can be expressed as follows:

$$\begin{aligned} -\varLambda (t)&=-\,\eta _{1}{\dot{x}}^{2}-\eta _{2}{\dot{y}}^{2}-\eta _{3}{\dot{z}} ^{2}-\eta _{4}{\dot{\gamma }}_{x}^{2}-\eta _{5}{\dot{\gamma }}_{y}^{2}\nonumber \\&\quad -\,\eta _{6} \rho ^{2}\left( \frac{e_{x}}{2}\right) \nonumber \\&\quad -\,\eta _{7}\rho ^{2}\left( \frac{e_{y}}{2}\right) -\eta _{8}\rho ^{2}\left( \frac{e_{z}}{2}\right) +\alpha k_{\text {d}\gamma _{x}}{\dot{\gamma }}_{x}{\dot{x}}\nonumber \\&\quad +\,\alpha k_{\text {d}\gamma _{y}}{\dot{\gamma }}_{y}{\dot{y}}-2m_{p}glC_{x}\left( \sin \frac{\gamma _{y}}{2}\cos \frac{\gamma _{y}}{2}\right) ^{2}\nonumber \\&\quad -\,2m_{p}glC_{y}\left( \sin \frac{\gamma _{x}}{2}\cos \frac{\gamma _{x}}{2}\right) ^{2} \end{aligned}$$
(64)

where \(\eta _{1}\), \(\eta _{2}\), \(\eta _{3}\), \(\eta _{4}\), \(\eta _{5}\), \(\eta _{6}\), \(\eta _{7}\), \(\eta _{8}\) are defined as

$$\begin{aligned} \left\{ \begin{array} [c]{l} \eta _{1}\overset{\varDelta }{=}\alpha k_{\text {d}x}-\frac{1}{2}k_{\text {d}x}^{2}-\frac{1}{2}\lambda _{M}-2m_{p}^{2}\\ \eta _{2}\overset{\varDelta }{=}\alpha k_{\text {d}y}-\frac{1}{2}k_{\text {d}y}^{2}-\frac{1}{2}\lambda _{M}-\frac{1}{4}m_{p}^{2}\\ \eta _{3}\overset{\varDelta }{=}\alpha k_{\text {d}z}-\frac{k_{\text {d}z}^{2}}{2}-\frac{1}{2}\lambda _{M}-2m_{p}^{2}\\ \eta _{4}\overset{\varDelta }{=}\alpha c_{\gamma _{x}}-\frac{1}{2}\lambda _{M} -2l^{2}-m_{p}l^{2}-\frac{1}{2}k_{\text {d}\gamma _{x}}^{2}\\ \eta _{5}\overset{\varDelta }{=}\alpha c_{\gamma _{y}}-\frac{1}{2}\lambda _{M} -3l^{2}-\frac{1}{2}k_{\text {d}\gamma _{y}}^{2}\\ \eta _{6}\overset{\varDelta }{=}2k_{px}+2\kappa _{x}\varsigma _{x}-1\\ \eta _{7}\overset{\varDelta }{=}2k_{py}+2\kappa _{y}\varsigma _{y}-1\\ \eta _{8}\overset{\varDelta }{=}2k_{pz}+2\kappa _{z}\varsigma _{z}-\frac{1}{2} \end{array} \right. . \end{aligned}$$
(65)

Now, we define S(t) as follows:

$$\begin{aligned} S&=-\,\eta _{1}{\dot{x}}^{2}-\eta _{2}{\dot{y}}^{2}-\eta _{3}{\dot{z}}^{2}-\eta _{4}{\dot{\gamma }}_{x}^{2}\nonumber \\&\quad -\eta _{5}{\dot{\gamma }}_{y}^{2}+\alpha k_{\text {d}\gamma _{x}}{\dot{\gamma }}_{x}\dot{x}+\alpha k_{\text {d}\gamma _{y}}{\dot{\gamma }}_{y}{\dot{y}}\nonumber \\&=sPs^{T} \end{aligned}$$
(66)

where P is a \(5\times 5\) matrix, \(s=\)\(\left[ \begin{array} [c]{ccccc} {\dot{x}}&{\dot{y}}&{\dot{z}}&{\dot{\gamma }}_{x}&{\dot{\gamma }}_{y} \end{array} \right] \in {\mathbb {R}}^{1\times 5}\) and denoted via the following equations

$$\begin{aligned} \left\{ \begin{array} [c]{l} P=\left[ \begin{array} [c]{ccccc} \eta _{1} &{} 0 &{} 0 &{} p_{14} &{} 0\\ 0 &{} \eta _{2} &{} 0 &{} 0 &{} p_{25}\\ 0 &{} 0 &{} \eta _{3} &{} 0 &{} 0\\ p_{41} &{} 0 &{} 0 &{} \eta _{4} &{} 0\\ 0 &{} p_{52} &{} 0 &{} 0 &{} \eta _{5} \end{array} \right] \\ p_{14}=p_{41}=-\,\frac{1}{2}\alpha k_{\text {d}\gamma _{x}}\text { }p_{25}=p_{52} =-\,\frac{1}{2}\alpha k_{\text {d}\gamma _{y}} \end{array} \right. , \end{aligned}$$
(67)

Consequently, S(t) in (66) can be described as

$$\begin{aligned} S=-\,{\dot{e}}^{T}(t)P{\dot{e}}(t). \end{aligned}$$
(68)

By substituting (68) into (64), we can obtain

$$\begin{aligned} -\varLambda (t)&=-\,{\dot{e}}^{T}(t)P{\dot{e}}(t)-\eta _{6}\rho ^{2}(\frac{e_{x}}{2})-\eta _{7}\rho ^{2}\left( \frac{e_{y}}{2}\right) \nonumber \\&\quad -\,\eta _{8}\rho ^{2}\left( \frac{e_{z}}{2}\right) -2m_{p}glC_{x}\left( \sin \frac{\gamma _{y}}{2}\cos \frac{\gamma _{y}}{2}\right) ^{2}\nonumber \\&\quad -\,2m_{p}glC_{y}\left( \sin \frac{\gamma _{x}}{2}\cos \frac{\gamma _{x}}{2}\right) ^{2}. \end{aligned}$$
(69)

In order to ensure \(-\varLambda (t)\) to be negative definite, it is implied that P need to be positive definite, then the conditions can denote as follows:

$$\begin{aligned} \left\{ \begin{array} [c]{l} \begin{array} [c]{ccc} \eta _{1}>0 &{} \eta _{1}\eta _{2}>0 &{} \eta _{1}\eta _{2}\eta _{3}>0 \end{array} \\ \begin{array} [c]{cc} \eta _{1}\eta _{2}\eta _{3}\eta _{4}+(\frac{1}{2}\alpha k_{\text {d}\gamma _{x}})^{2} \eta _{2}\eta _{3}>0 &{} \eta _{1}\eta _{2}\eta _{3}\eta _{4}\eta _{5}>0 \end{array} \end{array},\right. \nonumber \\ \end{aligned}$$
(70)

and it yields from (70) that

$$\begin{aligned} \begin{array} [c]{ccccc} \eta _{1}>0&\eta _{2}>0&\eta _{3}>0&\eta _{4}>0&\eta _{5}>0 \end{array} . \end{aligned}$$
(71)

Suppose that \(k_{\text {d}x}=k_{\text {d}y}=k_{\text {d}z}=\frac{1}{r}\alpha \) where \(r\in {\mathbb {R}}^{+}\) is positive constant. Hence, (71) can be denoted as

$$\begin{aligned} \left\{ \begin{array} [c]{l} \eta _{1}=\left( \frac{1}{r}-\frac{1}{2r^{2}}\right) \alpha ^{2}-\frac{1}{2}\lambda _{M}-2m_{p}^{2}>0\\ \eta _{2}=\left( \frac{1}{r}-\frac{1}{2r^{2}}\right) \alpha ^{2}-\frac{1}{2}\lambda _{M} -\frac{1}{4}m_{p}^{2}>0\\ \eta _{3}=\left( \frac{1}{r}-\frac{1}{2r^{2}}\right) \alpha ^{2}-\frac{1}{2}\lambda _{M}-2m_{p}^{2}>0\\ \eta _{4}=-\,\frac{1}{2r^{2}}\alpha ^{2}+c_{\gamma _{x}}\alpha -\frac{1}{2} \lambda _{M}-2l^{2}-m_{p}l^{2}>0\\ \eta _{5}=-\,\frac{1}{2r^{2}}\alpha ^{2}+c_{\gamma _{y}}\alpha -\frac{1}{2} \lambda _{M}-3l^{2}>0 \end{array} \right. . \end{aligned}$$
(72)

By making some mathematical manipulation, it is reduced as

$$\begin{aligned} \left\{ \begin{array} [c]{l} (2r-1)\alpha ^{2}>(\lambda _{M}+4m_{p}^{2})r^{2}\\ \alpha ^{2}-2r^{2}c_{\gamma _{x}}\alpha +r^{2}(4l^{2}+2m_{p}l^{2}+\lambda _{M})>0\\ \alpha ^{2}-2r^{2}c_{\gamma _{y}}\alpha +r^{2}(6l^{2}+\lambda _{M})>0 \end{array} \right. . \nonumber \\ \end{aligned}$$
(73)

Thus, \(\alpha \) should be selected to satisfy

$$\begin{aligned} \max \left\{ \beta _{1}\text { }\beta _{3}\text { }r\sqrt{\frac{\lambda _{M} +4m_{p}^{2}}{2r-1}}\right\}<\alpha <\min \left\{ \beta _{2}\text { }\beta _{4}\right\} \nonumber \\ \end{aligned}$$
(74)

with

$$\begin{aligned} \left\{ \begin{array} [c]{l} \beta _{1}=r^{2}c_{\gamma _{x}}-r\sqrt{r^{2}c_{\gamma _{x}}^{2}-(4l^{2}+2\bar{m}_{p}l^{2}+\lambda _{M})}\\ \beta _{2}=r^{2}c_{\gamma _{x}}+r\sqrt{r^{2}c_{\gamma _{x}}^{2}-(4l^{2}+2\bar{m}_{p}l^{2}+\lambda _{M})}\\ \beta _{3}=r^{2}c_{\gamma _{y}}-r\sqrt{r^{2}c_{\gamma _{y}}^{2}-(6l^{2} +\lambda _{M})}\\ \beta _{4}=r^{2}c_{\gamma _{y}}+r\sqrt{r^{2}c_{\gamma _{y}}^{2}-(6l^{2} +\lambda _{M})} \end{array} \right. , \nonumber \\ \end{aligned}$$
(75)

and r should be chosen to satisfy

$$\begin{aligned} r\ge \max \left\{ \frac{1}{2},\frac{\sqrt{4l^{2}+2{\bar{m}}_{p}l^{2}+\lambda _{M}} }{c_{\gamma _{x}}^{2}},\frac{\sqrt{6l^{2}+\lambda _{M}}}{c_{\gamma _{y}}^{2}}\right\} . \nonumber \\ \end{aligned}$$
(76)

On the other hand, considering the negative property of \(-\varLambda (t)\), the following inequalities hold

$$\begin{aligned} \left\{ \begin{array} [c]{l} \eta _{6}=2k_{py}+2\kappa _{x}\varsigma _{x}-1>0\\ \eta _{7}=2k_{py}+2\kappa _{y}\varsigma _{y}-1>0\\ \eta _{8}=2k_{pz}+2\kappa _{z}\varsigma _{z}-\frac{1}{2}>0 \end{array} \right. , \end{aligned}$$
(77)

that means

$$\begin{aligned} k_{px}>\frac{1}{2}\text { }k_{py}>\frac{1}{2}\text { }k_{pz}>\frac{1}{4}\text { }\kappa _{x}>0\text { }\kappa _{y}>0\text { }\kappa _{z}>0. \nonumber \\ \end{aligned}$$
(78)

Thus, combined with (32) and (34), we can obtain the conditions for \(\alpha \), \(k_{\text {d}x}\), \(k_{\text {d}y}\), \(k_{\text {d}z}\), \(k_{px}\), \(k_{py}\), \(k_{pz}\), \(k_{\text {d}\gamma _{x}}\), \(k_{\text {d}\gamma _{y}}\), \(\kappa _{x}\), \(\kappa _{y}\), \(\kappa _{z}\) in (49) and (50) based on (71)-(78).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xian, B., Wang, S. & Yang, S. Nonlinear adaptive control for an unmanned aerial payload transportation system: theory and experimental validation. Nonlinear Dyn 98, 1745–1760 (2019). https://doi.org/10.1007/s11071-019-05283-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-019-05283-0

Keywords

Navigation