Optimal Control of Pendubot System Using SIMATIC S7-1200

Van-Dat Nguyen 1*, Quang-Dong Dang 1, Minh-Tai Vo 2,3, Minh-Duc Tran 2, Anh-Khoa Vo 4, Thanh-Binh Nguyen 5, Phuoc-Tung Tran 5, Huu-Sang Le 5

1 Hung Yen University of Technology and Education (UTEHY)
Dan Tien, Khoai Chau District, Hung Yen Province, 16000, Vietnam
2 Ho Chi Minh city University of Technology (HCMUT), VNU-HCMC
Ly Thuong Kiet St., No. 268, District 10, Ho Chi Minh City, 700000, Vietnam
3 Royal Melbourne Institute of Technology (RMIT)
Nguyen Van Linh Blvd., No. 702, Tan Hung ward, District 7, HCMC, 700000, Vietnam.
4 Robert BOSCH Engineering and Business Solutions (BOSCH), Vietnam
Cong Hoa St., No. 364, Tan Binh District, Ho Chi Minh City, 700000, Vietnam
5 Ho Chi Minh city University of Technology and Education (HCMUTE)
Vo Van Ngan St., No. 01, Thu Duc City, Ho Chi Minh City, 700000, Vietnam

Robotica & Management, Vol. 28, No. 1, pp. 43-52
DOI: https://doi.org/10.24193/rm.2023.1.6

Abstract: : In optimal control, a common problem is developing a control law that can drive a dynamical system from one state to another as quickly as possible. The objective of paper is to design optimal control approach based SIMATIC S7-1200 for under-actuated Pendubot system. The main contribution of this paper is to introduce and validate control method for laboratory model of Pendubot system by using industrial device, namely, Programmable logic controller (PLC). Besides, Lyapunov Analysis is used to analyze the stability of optimal control. The simulation is performed in MATLAB/Simulink environment and experiment is carried out in TIA PORTAL environment. The simulation results and experimental results illustrate that the proposed control method is effective and robust by using PLC.

Keywords: Optimal control, SIMATIC S7-1200, Pendubot, PLC, underactuated system.

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References

[1] Vu D.D., Huynh X.D.P.V.K., Nguyen M.T., Nguyen V.D.H.: “A Method of Fuzzy-Sliding Mode Control for Pendubot Model”, The University of Danang – Journal of Science and Technology, vol. 11, No. 120.1, Nov. 2017, pp. 12-16, https://jst-ud.vn/jst-ud/article/view/3846.

[2] Tran H.C., Nguyen M.T., Nguyen V.D.H.: “Application of pid-fuzzy control for pendubot”, JTE, No. 44A, pp. 61–67, Oct. 2017, https://jte.hcmute.edu.vn/index.php/jte/article/view/379.

[3] Tran V.D.: “Thiết kế chế tạo và điều khiển ổn định hệ Pendubot”, School-level scientific research project, 2015.

[4] Pham V.L.: “Điều khiển cân bằng Pendubot”, Master thesis at HCMC University of Technology and Education, 2016.

[5] Block D., Spong M.: “Mechanical Design and Control of the Pendubot”, SAE Technical Paper 951199, 1995, https://doi.org/10.4271/951199.

[6] Xu J., Huzhen S.: “The Study on PenduBot Control Linear Quadratic Regulator and Particle Swarm Optimization.” J. Digit. Inf. Manag, Vol. 11, pp. 16-24, 2013.

[7] Djamila Z., Khier B.: “Optimal sliding mode control of the Pendubot”, Journal of Computer Science and Information Systems, Vol. 2, pp. 45-51, 2013.

[8] Fantoni I., Lozano R., Spong M.W.: “Energy based control of the Pendubot”, IEEE Transactions on Automatic Control, Vol. 45(4), pp. 725-729, 2000, DOI: 10.1109/9.847110.

[9] Rudra S., Barai R.K.: “Design of block backstepping based nonlinear state feedback controller for Pendubot”, 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), pp. 1-5, 2016, DOI: 10.1109/CMI.2016.7413794.

[10] Ma X.Q., Su C.Y.: “Theory and implementation of a fuzzy control scheme for Pendubot”, IFAC Proceedings Volumes, Vol.35(1), pp. 335-340, 2002, https://doi.org/10.3182/20020721-6-ES 1901.00868.

[11] Melin P., Castillo O.: “A New Method for Adaptive Control of Non-Linear Plants Using Type-2 Fuzzy Logic and Neural Networks”, International Journal of General Systems, Vol. 33, pp. 289-304, 2007, https://doi.org/10.1080/03081070310001633608.

[12] Xia D., Chai T., Wang L.: “Fuzzy Neural Network Friction Compensation-Based Singularity Avoidance Energy Swing-Up to Nonequilibrium Unstable Position Control of Pendubot”, Vol. 22(2), pp. 690-705, 2014, DOI: 10.1109/TCST.2013.2255290.

[13] Shibli M.: “Direct Adaptive Control for Underactuated Mechatronic Systems using Fuzzy Systems and Neural Networks: A Pendubot Case”, 2006 Canadian Conference on Electrical and Computer Engineering, pp. 1490-1493, 2006, DOI: 10.1109/CCECE.2006.277489.