Skip to main content
Log in

Adaptive fuzzy backstepping hierarchical sliding mode control for six degrees of freedom overhead crane

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

Most overhead crane control studies attempt to position the payload accurately and minimize its horizontal swing without considering axial oscillation. The axial vibration caused by the lifting rope’s elasticity significantly affects the actuators’ reliability and the system’s overall performance over time. In this paper, a novel overhead crane model is developed to describe an actual crane’s behavior more closely by further considering the effect of axial payload oscillation. Furthermore, an adaptive fuzzy backstepping hierarchical sliding mode controller is designed to guarantee precise movements and reduce vibrations of payload in both horizontal and vertical directions under complex conditions, such as unknown external disturbances and cable elasticity. Three inputs consisting of the trolley-moving force, the bridge-pulling force, and the payload-hoisting torque stabilize six outputs simultaneously, including trolley motion, bridge travel, hoisting drum rotation, two payload swings, and axial payload oscillation. The controller is first designed using the backstepping hierarchical sliding mode control strategy. This controller’s parameters are then adjusted online using a fuzzy logic system, ensuring system states’ stability on the sliding surface. The system’s stability is analyzed and proved mathematically by LaSalle’s principle. Several simulations on MATLAB/Simulink have been conducted with constant or trapezoidal reference signals, with and without external disturbances. These simulation results show the proposed method’s effectiveness, such as motion precision, minor load swings, and minimal axial oscillation.

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

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Almutairi NB, Zribi M (2009) Sliding mode control of a three-dimensional overhead crane. J Vib Control 15:1679–1730

    Article  MathSciNet  MATH  Google Scholar 

  2. Ramli L, Mohamed Z, Abdullahi A, Jaafar HI, Lazim IM (2017) Control strategies for crane systems: a comprehensive review. Mech Syst Sig Process 95:1–23

    Article  Google Scholar 

  3. Maghsoudi MJ, Ramli L, Sudin S, Mohamed Z, Husain AR, Wahid HB (2019) Improved unity magnitude input shaping scheme for sway control of an underactuated 3d overhead crane with hoisting. Mech Syst Sig Process 123:466–482

    Article  Google Scholar 

  4. Mohammed A, Alghanim K, Andani MT (2020) An adjustable zero vibration input shaping control scheme for overhead crane systems. Shock Vib. 2020:1–7

    Article  Google Scholar 

  5. Ahmad MA, Samin RE, Zawawi MA (2010) Comparison of optimal and intelligent sway control for a lab-scale rotary crane system. In: Second international conference on computer engineering and applications, vol 1, pp 229–234

  6. Jaafar HI, Mohamed Z, Subha NAM, Husain AR, Ismail FS, Ramli L, Tokhi MO, Shamsudin MA (2018) Efficient control of a nonlinear double-pendulum overhead crane with sensorless payload motion using an improved PSO-tuned PID controller. J. Vib. Control 25:907–921

    Article  MathSciNet  Google Scholar 

  7. Guo H, Feng Z, She J (2020) Discrete-time multivariable PID controller design with application to an overhead crane. Int. J. Syst. Sci. 51:2733–2745

    Article  MathSciNet  MATH  Google Scholar 

  8. Zhang M, Zhang Y, Cheng X (2019) An enhanced coupling PD with sliding mode control method for underactuated double-pendulum overhead crane systems. Int. J. Control Autom. Syst. 17:1579–1588

    Article  Google Scholar 

  9. Önen Ü (2017) Anti-swing control of an overhead crane by using genetic algorithm based LQR. Int. J. Eng. Comput. Sci. 6(6):21612–21616

    Google Scholar 

  10. Shao X, Zhang J, Zhang X (2019) Takagi-Sugeno fuzzy modeling and PSO-based robust LQR anti-swing control for overhead crane. Math Probl Eng. https://doi.org/10.1155/2019/4596782

    Article  MathSciNet  MATH  Google Scholar 

  11. Tuan LA, Lee SG, Dang VH, Moon SC, Kim B (2013) Partial feedback linearization control of a three-dimensional overhead crane. Int J Control Autom Syst 11:718–727

    Article  Google Scholar 

  12. Le TA, Lee SG, Moon SC (2014) Partial feedback linearization and sliding mode techniques for 2D crane control. Trans Inst Measurement Control 36:78–87

    Article  Google Scholar 

  13. Wu X, He X (2016) Partial feedback linearization control for 3-D underactuated overhead crane systems. ISA Trans 65:361–370

    Article  Google Scholar 

  14. Ospina-Henao PA, Carrillo-Suárez A, Guarín-Martínez L, Ortiz-Blanco AM, Peñaranda-Vega J (2021) Partial feedback linearization for a path control in a gantry crane used in lifting machinery in civil engineering. J. Phys. Conf. Ser. 1723(1):012049

    Article  Google Scholar 

  15. Böck M, Kugi A (2014) Real-time nonlinear model predictive path-following control of a laboratory tower crane. IEEE Trans. Control Syst. Technol. 22:1461–1473

    Article  Google Scholar 

  16. Wu Z, Xia X, Zhu B (2015) Model predictive control for improving operational efficiency of overhead cranes. Nonlinear Dyn 79:2639–2657

    Article  Google Scholar 

  17. Chen H, Fang Y, Sun N (2016) A swing constraint guaranteed MPC algorithm for underactuated overhead cranes. IEEE/ASME Trans Mechatron 21:2543–2555

    Article  Google Scholar 

  18. Qian D, Yi J, Zhao D (2008) Hierarchical sliding mode control for a class of SIMO under-actuated systems. Control Cybern 37:159–175

    MathSciNet  MATH  Google Scholar 

  19. Tuan LA, Moon SC, Kim DH, Lee SG (2012) Adaptive sliding mode control of three dimensional overhead cranes. In: 2012 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER) pp 354–359

  20. Chwa D (2017) Sliding-mode-control-based robust finite-time antisway tracking control of 3-D overhead cranes. IEEE Trans Ind Electron 64:6775–6784

    Article  Google Scholar 

  21. Lu B, Fang Y, Sun N (2017) Sliding mode control for underactuated overhead cranes suffering from both matched and unmatched disturbances. Mechatronics 47:116–125

    Article  Google Scholar 

  22. Xuan HL, Van TN, Viet AL, Thuy NBT, Xuan MP (2017) Adaptive backstepping hierarchical sliding mode control for uncertain 3D overhead crane systems. Int Conf Syst Sci Eng (ICSSE) 2017:438–443

    Google Scholar 

  23. Ye J, Reppa V, Negenborn R (2020) Backstepping control of heavy lift operations with crane vessels. IFAC-PapersOnLine 53:14704–14709

    Article  Google Scholar 

  24. Khudhair MY, Hassan MY, Kadhim SK (2021) Backstepping control strategy for overhead crane system. Eng Technol J 39:370–381

    Article  Google Scholar 

  25. Lee LH, Huang PH, Shih YC, Chiang TC, Chang CY (2014) Parallel neural network combined with sliding mode control in overhead crane control system. J Vib Control 20:749–760

    Article  Google Scholar 

  26. Anh LV, Hai LX, Thuan VD, Trieu PV, Tuan LA, Cuong HM (2018) Designing an adaptive controller for 3D overhead cranes using hierarchical sliding mode and neural network. Int Conf Syst Sci Eng (ICSSE) 2018:1–6

    Google Scholar 

  27. Le HX, Nguyen TV, Le AV, Phan TA, Nguyen N, Phan MX (2019) Adaptive hierarchical sliding mode control using neural network for uncertain 2D overhead crane. Int J Dyn Control 7(3):996–1004

    Article  MathSciNet  Google Scholar 

  28. Zhao X, Wang X, Zhang S, Zong G (2019) Adaptive neural backstepping control design for a class of nonsmooth nonlinear systems. IEEE Trans Syst Man Cybern Syst 49:1820–1831

    Article  Google Scholar 

  29. Tong S, Li Y, Shi P (2009) Fuzzy adaptive backstepping robust control for SISO nonlinear system with dynamic uncertainties. Inf Sci 179:1319–1332

    Article  MathSciNet  MATH  Google Scholar 

  30. Lin TC, Yeh JL, Hung TC, Kuo CH (2015) Direct adaptive fuzzy sliding mode PI tracking control of a three-dimensional overhead crane. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE) pp 1–8

  31. Nguyen VT, Yang C, Du C, Liao L (2019) Design and implementation of finite time sliding mode controller for fuzzy overhead crane system. ISA transactions

  32. de Aguiar CC, Leite DF, Pereira DA, Andonovski G, Skrjanc I (2021) Nonlinear modeling and robust LMI fuzzy control of overhead crane systems. J Frankl Inst 358:1376–1402

    Article  MathSciNet  MATH  Google Scholar 

  33. Duong SC, Uezato E, Kinjo H, Yamamoto T (2012) A hybrid evolutionary algorithm for recurrent neural network control of a three-dimensional tower crane. Autom Constr 23:55–63

    Article  Google Scholar 

  34. Le VA, Le HX, Nguyen L, Phan MX (2019) An efficient adaptive hierarchical sliding mode control strategy using neural networks for 3D overhead cranes. Int J Autom Comput 16(5):614–627

    Article  Google Scholar 

  35. Liu CA, Zhao HF, Cui Y (2014) Research on application of fuzzy adaptive PID controller in bridge crane control system. In: 2014 IEEE 5th international conference on software engineering and service science, pp 971–974

  36. Aguiar CC, Leite DF, Pereira DA, Andonovski G, Skrjanc I (2021) Nonlinear modeling and robust LMI fuzzy control of overhead crane systems. J Frankl Inst 358:1376–1402

    Article  MathSciNet  MATH  Google Scholar 

  37. Shen P, Schatz J, Caverly RJ (2021) Passivity-based adaptive trajectory control of an underactuated 3-DOF overhead crane. Control Eng Pract 112:104834

    Article  Google Scholar 

  38. Kim J, Kiss B, Kim D, Lee D (2021) Tracking control of overhead crane using output feedback with adaptive unscented Kalman filter and condition-based selective scaling. IEEE Access 9:108628–108639

    Article  Google Scholar 

  39. Xing X, Liu J (2021) Vibration and position control of overhead crane with three-dimensional variable length cable subject to input amplitude and rate constraints. IEEE Trans Syst Man Cybern Syst 51:4127–4138

    Article  Google Scholar 

  40. Cong B, Liu X, Chen Z (2012) Backstepping based adaptive sliding mode control for spacecraft attitude maneuvers. In: Proceedings of 2012 UKACC international conference on control, pp 1046–1051

  41. Wan M, Liu Q (2019) Adaptive fuzzy backstepping control for uncertain nonlinear systems with tracking error constraints. Adv Mech Eng. https://doi.org/10.1177/1687814019851309

    Article  Google Scholar 

  42. Song Z, Sun K (2014) Adaptive backstepping sliding mode control with fuzzy monitoring strategy for a kind of mechanical system. ISA Trans 53(1):125–33

    Article  Google Scholar 

  43. Adhikary N, Mahanta C (2013) Integral backstepping sliding mode control for underactuated systems: swing-up and stabilization of the cart-pendulum system. ISA Trans 52(6):870–80

    Article  Google Scholar 

  44. Huang YJ, Kuo TC, Chang SH (2008) Adaptive sliding-mode control for nonlinearsystems with uncertain parameters. IEEE Trans Syst Man Cybern Part B (Cybern) 38:534–539

    Article  Google Scholar 

  45. Fang Y, Fei J, Hu T (2018) Adaptive backstepping fuzzy sliding mode vibration control of flexible structure. J Low Freq Noise Vib Act Control 37:1079–1096

    Article  Google Scholar 

  46. Yoshimura T (2018) Design of adaptive fuzzy backstepping sliding mode control for MIMO uncertain discrete-time nonlinear systems based on noisy measurements. J Vib Control 24:393–406

    Article  MathSciNet  MATH  Google Scholar 

  47. Truong HVA, Tran DT, To XD, Ahn KK, Jin M (2019) Adaptive fuzzy backstepping sliding mode control for a 3-DOF hydraulic manipulator with nonlinear disturbance observer for large payload variation. Appl Sci 9(16):3290

    Article  Google Scholar 

  48. Zhang X, Lin H (2020) Backstepping fuzzy sliding mode control for the antiskid braking system of unmanned aerial vehicles. Electronics 9:1731

    Article  Google Scholar 

  49. Fei J, Fang Y, Yuan Z (2020) Adaptive fuzzy sliding mode control for a micro gyroscope with backstepping controller. Micromachines 11(11):968

    Article  Google Scholar 

  50. Meirovitch L (2010) Methods of analytical dynamics. Courier Corporation

  51. Lasalle JP (1964) Recent advances in Liapunov stability theory. SIAM Review 6:1–11

    Article  MathSciNet  MATH  Google Scholar 

  52. Zhao ZY, Tomizuka M, Isaka S (1993) Fuzzy gain scheduling of PID controllers. IEEE Trans Syst Man Cybern 23:1392–1398

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank colleagues in the High-performance simulation and computing (HPC) laboratory for their support during the experiments conducted at Hanoi University of Industry, Vietnam.

Funding

This work was supported and funded by the Hanoi University Of Industry under Grant 14-2021-RD/HD-DHCN.

Author information

Authors and Affiliations

Authors

Contributions

All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

Corresponding author

Correspondence to Thai Dinh Kim.

Ethics declarations

Conflict of interest

Authors state no conflict of interest.

Appendices

The coefficients of matrices

1. The coefficients of matrix M.

$$\begin{aligned}&l = \delta + \Delta \delta + r{\vartheta _x} + r\psi \\ {m_{11}}&= {m_c} + {m_t}\\ {m_{12}}&= {m_{21}} = {m_{51}} = {m_{15}} = 0\\ {m_{13}}&= {m_{31}} = {m_c}r\sin \left( {{\vartheta _x}} \right) \\ {m_{14}}&= {m_{41}} = {m_c}l\cos \left( {{\vartheta _x}} \right) \\ {m_{16}}&= {m_{61}} = {m_c}\sin \left( {{\vartheta _x}} \right) \\ {m_{22}}&= {m_c} + {m_b} + {m_t}\\ {m_{23}}&= {m_{32}} = {m_c}r\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ {m_{24}}&= {m_{42}} = \\&={m_c}\left( {r\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - l\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) } \right) \\ {m_{25}}&= {m_{52}} = {m_c}l\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \\ {m_{26}}&= {m_{62}} = {m_c}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ {m_{33}}&= J + {m_c}{r^2}\\ {m_{34}}&= {m_{43}} = {m_c}{r^2}{\text {co}}{{\text {s}}^2}\left( {{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) \\ {m_{35}}&= {m_{53}} = \frac{1}{4}{m_c}{r^2}\sin \left( {2{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) \\ {m_{36}}&= {m_{63}} = {m_c}r\\ {m_{44}}&= {m_c}\left( \begin{aligned} {l^2} + {r^2}{\sin ^2}\left( {{\vartheta _y}} \right) {\text {co}}{{\text {s}}^2}\left( {{\vartheta _x}} \right) - \\ r\delta {\sin ^2}\left( {{\vartheta _y}} \right) \sin \left( {2{\vartheta _x}} \right) \\ \end{aligned} \right) \\ {m_{45}}&= {m_{54}} = \frac{1}{2}{m_c}rl\sin \left( {2{\vartheta _y}} \right) {\text {cos}}\left( {2{\vartheta _x}} \right) \\ {m_{46}}&= {m_{64}} = {m_c}r{\sin ^2}\left( {{\vartheta _y}} \right) {\cos ^2}\left( {{\vartheta _x}} \right) \\ {m_{55}}&= {m_c}\left( \begin{aligned} {l^2}{\cos ^2}\left( {{\vartheta _x}} \right) + {r^2}{\sin ^2}\left( {{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) - \\ rl\sin \left( {2{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {m_{56}}&= {m_{65}} = \frac{1}{4}{m_c}r\sin \left( {2{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) \\ {m_{66}}&= {m_c} \end{aligned}$$

2. The coefficients of matrix C

$$\begin{aligned} {c_{11}}&= {c_{21}} = {c_{31}} = {c_{33}} = {c_{41}} = {c_{51}} = {c_{15}} = {c_{61}} = 0\\ {c_{12}}&= {c_{22}} = {c_{32}} = {c_{42}} = {c_{52}} = 0\\ {c_{62}}&= {c_{36}} = {c_{63}} = {c_{66}} = 0\\ {c_{13}}&= 2{m_c}r{{\dot{\vartheta }} _x}\cos \left( {{\vartheta _x}} \right) \\ {c_{14}}&= {m_c}\left( {r{{{\dot{\vartheta }} }_x}\cos \left( {{\vartheta _x}} \right) - l{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) } \right) \\ {c_{16}}&= 2{m_c}{{\dot{\vartheta }} _x}\cos \left( {{\vartheta _x}} \right) \\ {c_{23}}&= 2{m_c}r\left( \begin{aligned} {{\dot{\vartheta }} }_y\cos \left( {\vartheta _x} \right) \cos \left( {\vartheta _y} \right) - \\ {{\dot{\vartheta }} }_x\sin \left( {\vartheta _x} \right) \sin \left( {\vartheta _y} \right) \\ \end{aligned} \right) \\ {c_{24}}&= {m_c}\left( \begin{aligned} 2r{{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) - \\ 2r{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - \\ l{{{\dot{\vartheta }} }_x}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{25}}&= - {m_c}l\left( \begin{aligned} 2{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) + \\ {{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{26}}&= 2{m_c}\left( {{{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) - {{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) } \right) \\ {c_{34}}&= {m_c}r\left( \begin{aligned} - l{{{\dot{\vartheta }} }_x} + r{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) + \\ \frac{l}{2}{{{\dot{\vartheta }} }_y}\sin \left( {2{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) + \\ r{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _x}} \right) - \\ \frac{r}{2}{{{\dot{\vartheta }} }_x}\sin \left( {2{\vartheta _x}} \right) \left( {1 + {{\sin }^2}\left( {{\vartheta _x}} \right) } \right) \\ \end{aligned} \right) \\ {c_{35}}&= {m_c}r\left( \begin{aligned} \frac{r}{2}{{{\dot{\vartheta }} }_y}\sin \left( {2{\vartheta _x}} \right) {\cos ^2}\left( {{\vartheta _y}} \right) -\\ l{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) - \\ \frac{l}{2}{{{\dot{\vartheta }} }_x}\sin \left( {2{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{43}}&= {m_c}r\left( \begin{aligned} 2l{{{\dot{\vartheta }} }_x} + r{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) - \\ r{{{\dot{\vartheta }} }_x}\sin \left( {2{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{44}}&= {m_c}\left( \begin{aligned} - \left( \begin{aligned} l\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - \\ r\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \times \\ \left( \begin{aligned} 2r{{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) - \\ 2r{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - \\ l {{{\dot{\vartheta }} }_x}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) + \\ lr{{{\dot{\vartheta }} }_x}{\cos ^2}\left( {{\vartheta _x}} \right) - {l^2}{{{\dot{\vartheta }} }_x}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _x}} \right) - \\ {l^2}{{{\dot{\vartheta }} }_y}{\sin ^2}\left( {{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) \\ + \frac{{{l^2}}}{2}{{{\dot{\vartheta }} }_x}\sin \left( {2{\vartheta _x}} \right) {\cos ^2}\left( {{\vartheta _y}} \right) + \\ lr{{{\dot{\vartheta }} }_x}{\sin ^2}\left( {{\vartheta _x}} \right) {\cos ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{45}}&= {m_c}\left( \begin{aligned} \left( \begin{aligned} {l^2}\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - \\ rl\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \times \\ \left( \begin{aligned} 2{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) + \\ {{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) + \\ {l^2}{{{\dot{\vartheta }} }_y}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _x}} \right) {\cos ^2}\left( {{\vartheta _y}} \right) - \\ rl{{{\dot{\vartheta }} }_y}{\sin ^2}\left( {{\vartheta _x}} \right) {\cos ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{46}}&= {m_c}\left( \begin{aligned} 2l{{{\dot{\vartheta }} }_x} + r{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) - \\ r{{{\dot{\vartheta }} }_x}\sin \left( {2{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{53}}&= 2{m_c}r\left( \begin{aligned} l{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) - \\ r{{{\dot{\vartheta }} }_x}{\sin ^2}\left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \sin \left( {{\vartheta _y}} \right) - \\ r{{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{54}}&= {m_c}\left( \begin{aligned} l\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \times \\ \left( \begin{aligned} 2r\left( \begin{aligned} {{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) - \\ {{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) - \\ l {{{\dot{\vartheta }} }_x}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ \left( {r\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - l\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) } \right) \times \\ \left( \begin{aligned} l\left( \begin{aligned} {{{\dot{\vartheta }} }_x}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) - \\ 2{{{\dot{\vartheta }} }_y}\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) + \\ r{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ \end{aligned} \right) \\ {c_{55}}&= {m_c}\left( \begin{aligned} \left( \begin{aligned} r{{{\dot{\vartheta }} }_y}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) - \\ l{{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \times \\ \left( \begin{aligned} r\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) - \\ l\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) - \\ {l^2}\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \times \\ \left( \begin{aligned} 2{{{\dot{\vartheta }} }_x}\sin \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) + \\ {{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ \end{aligned} \right) \\ {c_{56}}&= {m_c}\left( \begin{aligned} 2l{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) - r{{{\dot{\vartheta }} }_x}{\sin ^2}\left( {{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) - \\ r{{{\dot{\vartheta }} }_y}\sin \left( {2{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{64}}&= {m_c}\left( \begin{aligned} - l{{{\dot{\vartheta }} }_x} - \frac{1}{2}r{{{\dot{\vartheta }} }_x}\sin \left( {2{\vartheta _x}} \right) {\sin ^2}\left( {{\vartheta _y}} \right) + \\ \frac{1}{2}l{{{\dot{\vartheta }} }_y}\sin \left( {2{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) + \\ r{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) \\ \end{aligned} \right) \\ {c_{65}}&= {m_c}\left( \begin{aligned} - l{{{\dot{\vartheta }} }_y}{\cos ^2}\left( {{\vartheta _x}} \right) + r{{{\dot{\vartheta }} }_y}\cos \left( {{\vartheta _x}} \right) \times \\ \sin \left( {{\vartheta _y}} \right) {\cos ^2}\left( {{\vartheta _y}} \right) - \\ \frac{l}{2}{{{\dot{\vartheta }}}_{x}}\sin \left( {2{\vartheta _x}} \right) \sin \left( {2{\vartheta _y}} \right) \\ \end{aligned} \right) \end{aligned}$$

3. The coefficients of vector G.

$$\begin{aligned} {g_1}&= {m_c}gr\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) \\ {g_2}&= - {m_c}gl\sin \left( {{\vartheta _x}} \right) {\text {cos}}\left( {{\vartheta _y}} \right) \\ {g_3}&= - {m_c}g\left( {l\sin \left( {{\vartheta _y}} \right) {\text {cos}}\left( {{\vartheta _x}} \right) - r\sin \left( {{\vartheta _x}} \right) \sin \left( {{\vartheta _y}} \right) } \right) \\ {g_4}&= {m_c}g\cos \left( {{\vartheta _x}} \right) \cos \left( {{\vartheta _y}} \right) + \rho \Delta \delta \end{aligned}$$

Proof of system stability

Let’s begin with following assumption.

Assumption 1

The backstepping control errors \({\xi _1},{\text { }}{\xi _2}\) in (19) and its time derivative are bounded, i.e., there exists a large enough number \(A \in {\mathbb {R}}\) such that \( {\left\| {{\xi _1}} \right\| }, {\left\| {{\xi _2}} \right\| }, {\left\| {{{{\dot{\xi }} }_1}} \right\| },{\left\| {{{{\dot{\xi }} }_2}} \right\| } \leqslant A < + \infty \)

This assumption is suitable for most natural systems, as states have a certain allowable limit.

Theorem 2

If the control law is defined as in (38) and the sliding surfaces are defined as in (25), (34), then, the subsystems and the entire system in (24) will stabilize asymptotically at the origin, i.e.,

1) \(\mathop {\lim }\limits _{t \rightarrow \infty } {\xi _1} = {0} ,{\text { }}\mathop {\lim }\limits _{t \rightarrow \infty } {\xi _2} = {0}\)

2) \(\mathop {\lim }\limits _{t \rightarrow \infty } S = {0} \)

where \( u_{11\mathrm{eq}} \) and \( u_{12\mathrm{eq}} \) are defined as in (32) and (33), respectively; \( u_{11\mathrm{sw}} \) and \( u_{12\mathrm{sw}} \) are determined to satisfy (43).

Proof

Applying the inequality of the quadratic form to (44), it yields:

$$\begin{aligned} {\dot{V}}_S&= - {S^T}\Upsilon {\lambda _1}S - {S^T}\Upsilon {\lambda _2}\mathrm{sign}\left( S \right) \nonumber \\&\leqslant - \lambda _{\min }^{\Upsilon {\lambda _1}}{\left\| S \right\| ^2} - \eta {S^T}\mathrm{sign}(S) \leqslant 0 \end{aligned}$$
(B1)

where \(\lambda _{\min }^{\Upsilon {\lambda _1}}\) is the smallest eigenvalue of the coefficient matrix \(\Upsilon {\lambda _1}\), and \(\eta \) is any eigenvalue of the matrix \(\Upsilon {\lambda _2}\).

Integrating both sides of (B1) with time, one obtains:

$$\begin{aligned} \int _0^t {{{\dot{V}}_S}d\tau }&\leqslant - \lambda _{\min }^{\Upsilon {\lambda _1}}\int _0^t {{\left\| S \right\| }^2}d\tau \nonumber \\ {}&- \eta \int _0^t {S^T}\mathrm{sign}(S) d\tau \nonumber \\ {V_S}\left( t \right)&\leqslant {V_S}\left( 0 \right) - \lambda _{\min }^{\Upsilon {\lambda _1}}\int _0^t {{\left\| S \right\| }^2}d\tau \nonumber \\ - \eta \int _0^t {S^T}\mathrm{sign}(S) d\tau&\leqslant {V_S}\left( 0 \right) \end{aligned}$$
(B2)

\(\square \)

From (35) and (36), it can be deduced that:

$$\begin{aligned} \lambda _{\min }^\Upsilon {\left\| S \right\| ^2}&\leqslant \frac{1}{2}{\left( {\int _0^t {Sd\tau } } \right) ^T}\Upsilon {\left( {1 - \gamma } \right) ^p}\Xi \int _0^t {Sd\tau } \nonumber \\&+\frac{1}{2}{S^T}\Upsilon S \leqslant {V_S}\left( 0 \right) \end{aligned}$$
(B3)

Inequality in (B3) shows that the energy inside the system is constantly decreasing. It can be seen that if \(\left\| S \right\| \rightarrow 0\) as \(t \rightarrow \infty \), the system state gradually approaches and stabilizes at its reference state.

In addition, given Assumption, it can be clearly seen that:

$$\begin{aligned} \left\| S \right\| = \left\| {{\alpha _1}{\xi _1} + {\alpha _2}{\xi _2}} \right\| \leqslant \left\| {{\alpha _1}{\xi _1}} \right\| + \left\| {{\alpha _2}{\xi _2}} \right\| < +\infty \end{aligned}$$
(B4)

Considering the following expressions:

$$\begin{aligned} \left\{ {\begin{matrix} {{\Phi _1} = {c_1}{\xi _1} + {\xi _2}} \\ {{\Phi _2} = {c_2}{\xi _1} + {\xi _2}} \end{matrix}} \right. \end{aligned}$$
(B5)

where \({c_1},{c_2} \in {\mathbb {R}^{3 \times 3}}\) are any positive diagonal matrices, and \({c_1} \ne {c_2}\).

Without loss of generality, it may be assumed that \(\left\| {{c_1}} \right\| > \left\| {{c_2}} \right\| \).

Based on the inequality of the quadratic form, it can be deduced that:

$$\begin{aligned}&\begin{aligned} 0 \leqslant {\left( {\int _0^{ + \infty } {{\Phi _1}d\tau } } \right) ^T}\int _0^{ + \infty } {{\Phi _1}d\tau } \leqslant \int _0^{ + \infty } {\Phi _1^T{\Phi _1}d\tau } \\ = \int _0^{ + \infty } {\left( {\xi _1^Tc_1^T{c_1}{\xi _1} + \xi _2^T{\xi _2}} \right) } d\tau + 2\int _0^{ + \infty } {\xi _1^Tc_1^T{\xi _2}d\tau } \\ \end{aligned} \\&\begin{aligned} 0 \leqslant {\left( {\int _0^{ + \infty } {{\Phi _2}d\tau } } \right) ^T}\int _0^{ + \infty } {{\Phi _2}d\tau } \leqslant \int _0^{ + \infty } {\Phi _2^T{\Phi _2}d\tau } \\ = \int _0^{ + \infty } {\left( {\xi _1^Tc_2^T{c_2}{\xi _1} + \xi _2^T{\xi _2}} \right) } d\tau + 2\int _0^{ + \infty } {\xi _1^Tc_2^T{\xi _2}} d\tau \\ \end{aligned} \end{aligned}$$

On the other hand,

$$\begin{aligned}&\int _0^{ + \infty } {\left( {\Phi _1^T{\Phi _1} - \Phi _2^T{\Phi _2}} \right) d\tau } \nonumber \\&=\int _0^{ + \infty } {\xi _1^T\left( {c_1^T{c_1} - c_2^T{c_2}} \right) {\xi _1}d\tau }+ \nonumber \\&\quad + 2\int _0^{ + \infty } {\xi _1^T\left( {c_1^T - c_2^T} \right) } {\xi _2}d\tau \end{aligned}$$
(B6)

Substituting \({\xi _2} = {\Phi _1} - {c_1}{\xi _1}\) into (B6), and note that \(c_2^T{c_1} = c_1^T{c_2}\), simplifying yields:

$$\begin{aligned}&\int _0^{ + \infty } {\left( {\Phi _1^T{\Phi _1} - \Phi _2^T{\Phi _2}} \right) d\tau } \nonumber \\&=2\int _0^{ + \infty } {{{\xi }}{{_1^T}}\left( {c_1^T - c_2^T} \right) {\Phi _1}d\tau }+ \nonumber \\&+\quad \int _0^{ + \infty } {{{\xi }}_1^T{{\left( {{c_1} - {c_2}} \right) }^T}\left( {{c_1} - {c_2}} \right) {{{\xi }}_1}d\tau } \end{aligned}$$
(B7)

Because \(\left\| {{c_1}} \right\| > \left\| {{c_2}} \right\| \), so we have

$$\begin{aligned} \int _0^{ + \infty } {\left( {\Phi _1^T{\Phi _1} - \Phi _2^T{\Phi _2}} \right) d\tau > 0} \end{aligned}$$
(B8)

Let \(\Delta {c^T} = c_1^T - c_2^T\) be a positive diagonal matrix. Then

$$\begin{aligned} \int _0^{ + \infty } {\xi _1^T\Delta {c^T}\Delta c{\xi _1}d\tau }&\leqslant 2\int _0^{ + \infty } {\xi _1^T\Delta {c^T}{\Phi _1}d\tau } \nonumber \\&\leqslant 2\int _0^{ + \infty } {\sup \left( {\xi _1^T\Delta {c^T}{\Phi _1}} \right) d\tau } \end{aligned}$$
(B9)

Inequality of quadratic form leads to

$$\begin{aligned} \xi _1^T\Delta {c^T}{c_1}{\xi _1} \leqslant \lambda _{\max }^{\Delta {c^T}{c_1}}{\left\| {{\xi _1}} \right\| ^2} \end{aligned}$$

where \(\lambda _{\max }^{\Delta {c^T}{c_1}}\) is the largest eigenvalue of the matrix \(\Delta {c^T}{c_1}\).

It can be deduced from (B8) that:

$$\begin{aligned} \int _0^{ + \infty } {\xi _1^T\Delta {c^T}\Delta c{\xi _1}d\tau } \leqslant 2\int _0^{ + \infty } {\sup \left( {\xi {{_1^T}}\Delta {c^T}{\Phi _1}} \right) d\tau } \nonumber \\ \leqslant {\left\| {2\lambda _{\max }^{\Delta {c^T}{c_1}}{{\left\| {{\xi _1}} \right\| }^2}} \right\| _1} + \left\| {\xi _1^T\Delta {c^T}{\xi _2}} \right\| _1 < + \infty \end{aligned}$$
(B10)

Note that \(\Delta {c^T} = c_1^T - c_2^T\) is a constant. Thus, there exists a constant \({\partial _1} > 0\) such that:

$$\begin{aligned} \int _0^{ + \infty } {\xi _1^T{\xi _1}d\tau } \leqslant {\partial _1} < + \infty \end{aligned}$$
(B11)

A completely similar proof, one can infer that there exists a positive constant \( {\partial _2} \) such that:

$$\begin{aligned} \int _0^{ + \infty } {\xi _2^T{\xi _2}d\tau } \leqslant {\partial _2} < + \infty \end{aligned}$$
(B12)

According to the convergence theory of the Barbalat integral, one can deduce that: \(\mathop {\lim }\limits _{t \rightarrow \infty } {\xi _1} = {0} ,{\text { }}\mathop {\lim }\limits _{t \rightarrow \infty } {\xi _2} = {0} \).

On the other hand, \( {\alpha _1} \) and \( {\alpha _2} \) are preselected coefficients (finite). Hence, from (34) it can be easily deduced that \(\mathop {\lim }\limits _{t \rightarrow \infty } {S} = {0}.\)

Theorem 1 has been proved.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Le, H.X., Kim, T.D., Hoang, QD. et al. Adaptive fuzzy backstepping hierarchical sliding mode control for six degrees of freedom overhead crane. Int. J. Dynam. Control 10, 2174–2192 (2022). https://doi.org/10.1007/s40435-022-00945-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-022-00945-1

Keywords

Navigation