Abstract
This study presents an adaptive controller based on neuro-fuzzy networks and stochastic approximation techniques. The algorithm assumes that the mathematical model of the plant is unknown. An adaptive Fuzzy Rule Emulated Network (FREN) structure is implemented as the main controller. While, a modified version of the Simultaneous Perturbation Stochastic Approximation (SPSA) technique is added as the adaptation algorithm, which estimates the gradient of the plant with respect to the control effort. The proposed FREN+SPSA performance for position control is compared to conventional FREN and classical PID controllers. Experimental tests were performed on a cartesian robotic system, regulating the frequency of a DC motor to follow a desired trajectory. Experimental results show better performance of the proposed FREN+SPSA controller than the conventional FREN and PID controller.
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References
Armendariz, J., Treesatayapun, C., Baltazar, A.: Force feedback controller based on fuzzy-rules emulated networks and Hertzian contact with ultrasound. Mech. Syst. Sig. Process. 27, 534–550 (2012)
Chen, M., Linkens, D.A.: A hybrid neuro-fuzzy PID controller. Fuzzy Sets Syst. 99(1), 27–36 (1998)
Dong, N., Wu, C.H., Gao, Z.K., Chen, Z.Q., Ip, W.H.: Data-driven control based on simultaneous perturbation stochastic approximation with adaptive weighted gradient estimation. IET Control Theory Appl. 10(2), 201–209 (2016)
Facundo, L., Gómez, J., Treesatayapun, C., Morales, A., Baltazar, A.: Adaptive control with sliding mode on a double fuzzy rule emulated network structure. IFAC-PapersOnLine 51(13), 609–614 (2018)
Hahn, B., Oldham, K.R.: A model-free on-off iterative adaptive controller based on stochastic approximation. IEEE Trans. Control Syst. Technol. 20(1), 196–204 (2011)
Hou, Z.S., Wang, Z.: From model-based control to data-driven control: survey, classification and perspective. Inf. Sci. 235, 3–35 (2013)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. IEEE Trans. Autom. Control 42(10), 1482–1484 (1997)
Kiefer, J., Wolfowitz, J., et al.: Stochastic estimation of the maximum of a regression function. Ann. Math. Stat. 23(3), 462–466 (1952)
Lee, C.H., Teng, C.C.: Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans. Fuzzy Syst. 8(4), 349–366 (2000)
Lin, C.T., Lee, C.G., Lin, C.T., Lin, C.: Neural Fuzzy Systems: A Neuro-fuzzy Synergism to Intelligent Systems, vol. 205. Prentice hall PTR, Upper Saddle River (1996)
Maeda, Y.: Real-time control and learning using neuro-controller via simultaneous perturbation for flexible arm system. In: Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), vol. 4, pp. 2583–2588. IEEE (2002)
Meng, D., Jia, Y., Du, J., Yu, F.: Data-driven control for relative degree systems via iterative learning. IEEE Trans. Neural Netw. 22(12), 2213–2225 (2011)
dos Santos Coelho, L., Pessôa, M.W., Sumar, R.R., Coelho, A.A.R.: Model-free adaptive control design using evolutionary-neural compensator. Expert Syst. Appl. 37(1), 499–508 (2010)
Shi, Y., Mizumoto, M.: An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules. Fuzzy Sets Syst. 118(2), 339–350 (2001)
Skogestad, S.: Probably the best simple PID tuning rules in the world. J. Process Control 1–28 (2001)
Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, vol. 65. Wiley, Hoboken (2005)
Spall, J.C., Chin, D.C.: Traffic-responsive signal timing for system-wide traffic control. Transp. Res. Part C: Emerg. Technol. 5(3–4), 153–163 (1997)
Spall, J.C., Cristion, J.A.: A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment. In: Proceedings of 1994 9th IEEE International Symposium on Intelligent Control, pp. 273–278. IEEE (1994)
Spall, J.C., et al.: Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Automatic Control 37(3), 332–341 (1992)
Treesatayapun, C., Uatrongjit, S.: Adaptive controller with fuzzy rules emulated structure and its applications. Eng. Appl. Artif. Intell. 18(5), 603–615 (2005)
Treesatayapun, C.: A discrete-time stable controller for an omni-directional mobile robot based on an approximated model. Control Eng. Practice 19(2), 194–203 (2011)
Treesatayapun, C.: Discrete-time direct adaptive control for robotic systems based on model-free and if-then rules operation. Int. J. Adv. Manuf. Technol. 68(1–4), 575–590 (2013)
Treesatayapun, C.: Adaptive control based on if-then rules for grasping force regulation with unknown contact mechanism. Robot. Comput.-Integr. Manuf. 30(1), 11–18 (2014)
Treesatayapun, C.: Stabilized adaptive controller based on direct if-then knowledge of electronic systems for PWM drivers. Electr. Eng. 98(1), 77–85 (2016)
Acknowledgments
This document is the result of the 257253 research project funded by CONACyT and the 28875 research project funded by SEP-PRODEP. The first author thanks CONACyT for her Ph.D. scholarship.
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Facundo, L., Treesatayapun, C., Baltazar, A. (2019). Adaptive Controller Based on IF-THEN Rules and Simultaneous Perturbation Stochastic Approximation Tuning for a Robotic System. In: MartÃnez-Villaseñor, L., Batyrshin, I., MarÃn-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_54
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