An Optimized Sliding Mode Controller for Gantry Crane System Based on Smell Agent Optimization Algorithm

Mohammed Buhari Mohammed, Thuku I. T., Peter Z. H., Sani S. K.

Abstract


Crane systems are a crucial part of the industrial machinery system used to transport heavy cargo or loads from one point to another. Technological advancement and operation environment requiring robust and high-speed operating cranes performing accurate position tracking irrespective of unwanted motion or disturbance is most required. However, this unwanted motion and disturbance is a crucial problem and a major setback in the control study of crane systems. Existing nonlinear controllers show a good system performance when compared to linear controllers whose performance deteriorates with an increase in operating range and uncertainty, hence, the need for a nonlinear controller that will guarantee system performance in the presence of uncertainty. This study implements a sliding mode controller (SMC) augmented with Smell Agent Optimization (SAO) Scheme for stabilization and tracking of position coordinates in a gantry crane system (GCS). Modelling and simulation of the GCS with the developed controller were carried out using MATLAB/Simulink R2019b. The performance of the GCS with the developed controller was evaluated based on steady-state error, overshoot, and settling time as performance metrics and results compared with the SMC controller reported in the literature. The results analysis illustrates that the SMC-SAO outperformed the standard SMC controller in terms of system stabilization and set point tracking. SMC-SAO achieved a 46.12% and 83.50% reduction in settling time in terms of cart position and swing angle respectively. Also, SMC-SAO achieved a 99.99% reduction in steady-state error to swing angle.


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References


Abdel-Rahman, E. M., Nayfeh, A. H., & Masoud, Z. N. J. M. A. (2003). Dynamics and control of cranes: A review. Journal of Vibration and Control, 9(7), 863-908. DOI: DOI: 10.1177/1077546303009007007

Abdullahi, A. M., Mohamed, Z., Selamat, H., Pota, H. R., Abidin, M. Z., Ismail, F., . . . Processing, S. (2018). Adaptive output-based command shaping for sway control of a 3D overhead crane with payload hoisting and wind disturbance. 98, 157-172.

Alagoz, B. B., Deniz, F. N., Keles, C., and Tan, N. (2015) Disturbance rejection performance analyses of closed-loop control systems by reference to disturbance ratio. ISA transactions, 55, 63.

Alphinas, R. A., Hansen, H. H., and Tambo, T. (2017). Comparison of the conventional closed-loop controller with an adaptive controller for a disturbed thermodynamic system. Paper presented at the 2017 Evolving and Adaptive Intelligent Systems (EAIS).

Astolfi, A., & Ortega, R. J. I. T. o. A. c. (2003). Immersion and invariance: a new tool for stabilization and adaptive control of nonlinear systems. 48(4), 590-606.

Chwa, D. J. I. T. o. I. E. (2017). Sliding-Mode-Control-Based Robust Finite-Time Antisway Tracking Control of 3-D Overhead Cranes. 64(8), 6775-6784.

D'Andréa-Novel, B., Boustany, F., & Conrad, F. (1992). Control of an overhead crane: Stabilization of flexibilities Boundary control and boundary variation (pp. 1-26): Springer.

d'Andréa-Novel, B., Boustany, F., Conrad, F., Rao, B. J. M. o. C., Signals, & Systems. (1994). Feedback stabilization of a hybrid PDE-ODE system: Application to an overhead crane. 7(1), 1-22.

d’Andrea-Novel, B., Boustany, F., & Rao, B. (1991). Control of an overhead crane: Feedback stabilization of a hybrid PDE-ODE system. Paper presented at the Proceedings of the 1st European Control Conference: ECC.

Emad. Q. Hussein, Ayad. Q Al-Dujaili & Ahmed, R., Ajel. (2020). Design of sliding mode control for overhead crane systems: Theory and experimentation. Paper presented at the 3rd International Conference on Sustainable Engineering Technique, Proceedings.

He, X., Shi, J., He, W., & Sun, C. J. I.-P. (2017). Boundary vibration control of a variable length crane system in two-dimensional space with output constraints. 50(1), 11996-12001. .

Kang, H., Shunqiang, S., Shengchao, Z., Xinfang, G., & Yongqi, Z. J. P. o. t. I. o. M. E., Part E: Journal of Process Mechanical Engineering. (2017). Dynamic analysis and tracking trajectory control of a crane. 231(5), 1045-1052.

Li, S., Liu, X., Dong, X., Jing, Y., Liu, X. J. I. J. o. I. C., Information, & Control. (2013). A novel controller for a class of nonlinear systems via immersion and invariance. 9(8), 3463-3470.

Liu, X., Li, W., Wang, W., & Xu, Z. J. A. J. o. C. (2018). Control for the New Harsh sea Conditions Salvage Crane Based on Modified Fuzzy PID.

Lu Biao, Fang Yongchun, & Ning, S. (2018). Modelling and nonlinear coordination control for an underactuated dual overhead crane system. Automatica, 91, 244-255.

Naskar, I., & Pal, A. (2017). Type-2 Fuzzy Controller with Type-1 Tuning Scheme for Overhead Crane Control. Paper presented at the International Conference on Computational Intelligence, Communications, and Business Analytics.

Nguyen, N. P., Ngo, Q. H., & Nguyen, C. N. (2017). Adaptive sliding mode control using radial basis function network for container cranes. Paper presented at the Control, Automation and Systems (ICCAS), 2017 17th International Conference on.

Ogata, K., & Yang, Y. (2002). Modern control engineering (Vol. 4): Prentice hall India.

Pauluk, M. J. P. E. (2016). Optimal and robust control of the 3D crane. 92(2), 206--212.

Renuka, V., & Mathew, A. T. J. I. J. T. A. R. M. E. (2013). Precise Modelling of a Gantry Crane System Including Friction 3D Angular Swing and Hoisting Cable Flexibility. 2, 119-125.

Salawudeen, A. T., Mu’azu, M. B., Yusuf, A., & Adedokun, A. E. (2021). A Novel Smell Agent Optimization (SAO): An extensive CEC study and engineering application. Knowledge-Based Systems, 232, 107486.

Yoon, J., Nation, S., Singhose, W., & Vaughan, J. E. J. I. T. o. C. S. T. (2014). Control of crane payloads that bounce during hoisting. 22(3), 1233-1238.


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