Abstract
In this paper, we proposed a real-coded genetic solution for a Fuzzy Sliding Mode Controller (FSMC) of a nonlinear inverted pendulum system to stabilize the pole angle and avoid the chattering phenomenon. To get the best performances of the system, we need to tune simultaneously a large number of controller parameters to satisfy a cost function. A manual tuning design would be time-consuming. The genetic algorithm based on the Darwinian principle of evolution is used to overcome these difficulties providing an automatic tuning scheme for all the parameters of the FSMC where a fitness function is developed reflecting a minimum steady-state error with fast rise time and low overshoot. Parameters tuned are the switching gain for sliding mode, the membership functions for inputs/output, and the rules base of the fuzzy logic controller (FLC). The efficiency of the proposed genetic tuning method is tested and compared with the conventional method with free and additional disturbances. Simulation results have shown the advantages of automatic tuning of the FSMC's parameters to achieve the desired results. The superiority of the tuned controller has been proved to control the pole angle of the inverted pendulum despite the presence of additional disturbances.
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References
Banerjee, R., Pal, A.: Stabilization of inverted pendulum on cart based on pole placement and LQR. In: International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), pp. 1–5. Kottayam, India (2018)
Chen, P., Chen, C., Chiang, W.: GA-based fuzzy sliding mode controller for nonlinear systems. Mathematical Problems in Engineering 2008, 1–16 (2008)
Chen, P., Chen, C., Chiang, W.: GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems. Expert Systems with Applications 36, 5872–5879 (2009)
Chin, C., Lin, W.: Robust genetic algorithm and fuzzy inference mechanism embedded in a sliding-mode controller for an uncertain underwater robot. IEEE/ASME Transactions on Mechatronics 23(2), 655–666 (2018)
Dotoli, M., Maione, B., Naso, D. Evolutionary Techniques for Tuning Fuzzy Sliding Mode Controllers. In: Masulli, F., Parenti, R., Pasi, G. (eds.) Advances in Fuzzy Systems and Intelligent Technologies. Shaker Publishing (2000)
Dotoli, M.: Fuzzy sliding mode control with piecewise linear switching manifold. Asian Journal of Control 5(4), 528–542 (2003)
Farzdaq, R., Mina, Q.: Chattering attenuation of sliding mode controller using genetic algorithm and fuzzy logic techniques. Eng. & Tech. Journal 27(14), 2595–2610 (2009)
Guo, L., Zheng, C.: Optimization of fuzzy sliding mode controller with improved genetic algorithm. In: 22nd International Conference on Electrical Machines and Systems (ICEMS). Harbin, China (2019)
Ji-Chang, L., Ya-Hu, K.: Decoupled fuzzy sliding-mode control. In: IEEE Trans on Fuzzy Systems 6(3) (1998)
Kapoor, N., Ohri, J.: Integrating a few actions for chattering reduction and error convergence in sliding mode controller in robotic manipulator. IJERT 2(5), 466–472 (2013)
Katsuhiko, O.: Modern Control Engineering. Pearson (2009)
Kawamura, A., Itoh, H., Sakamoto, K.: Chattering reduction of disturbance observer-based sliding mode control. IEEE Transactions on Industry Applications 30(2), 456–461 (1994)
Khalil, S.: Inverted pendulum analysis, design and implementation. The Instrumentation and Control Lab at the Institute of Industrial Electronics Engineering, Pakistan (2003)
Moshiri, B., Jalili-Kharaajoo, M., Besharati, F.: Application of fuzzy sliding mode based on genetic algorithms to control of robotic manipulators. In: IEEE Conference on Emerging Technologies and Factory Automation (2003)
Palm, R., Driankov, D., Hellendoorn, H.: Model Based Fuzzy Control. Springer-Verlag (1997)
Slotine, J., Sastry, S.: Tracking control of nonlinear systems using sliding surfaces with application to robot manipulator. International Journal of Control 38, 931–938 (1983)
Slotine, J., Li, W.: Applied Nonlinear Control. Prentice-Hall, New Jersy (1991)
Spyros, G., Gerosimos, G.: Design and stability analysis of a new Sliding mode Fuzzy logic Controller of reduced Complexity. Machine Intelligence & Robotic Control 1(1) (1999)
Wang, W.: Adaptive fuzzy sliding mode control for inverted pendulum. In: Proceedings of the Second Symposium International Computer Science and Computational Technology (ISCSCT’09), vol. 26, pp. 231–234 (2009)
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Mohammed Djaouti, S., Khelfi, M.F., Malki, M. (2023). Global Automatic Tuning of Fuzzy Sliding Mode Controller for an Inverted Pendulum: A Genetic Solution. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_14
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