Abstract
In this paper, a new intelligent robot motion control architecture – a highly accurate model-free fuzzy motion control- is proposed in order to achieve improved robot motion accuracy and dynamic performance. Its architecture combines a Mamdani fuzzy proportional (P) and a conventional integral (I) plus derivative (D) controller for the feedback part of the system, and a Takagi-Sugeno-Kang fuzzy controller for the feed-forward, nonlinear part. The fuzzy P + ID controller improves the performance of the nonlinear system, and the TSK fuzzy controller uses a TSK fuzzy inference system based on extended subtractive- clustering method which integrates information on joint angular displacement, velocity and acceleration for torque identification. The advantage of this kind of model-free control is that it uses the information directly from the input/output of the nonlinear system, without any complex robot model computation, in order to decrease the control system’s sensitivity to any dynamical uncertainty. Furthermore, parametric search for clustering parameters in extended subtractive clustering secures the high accuracy of the system identification. Consequently, this proposed model-free fuzzy motion control benefits from the advantages of two kinds of fuzzy system. It not only incorporates flexible design, good performance and simple conception but also ensures precise motion control and great robustness. Comparisons with other intelligent models and results from numerical studies on a 4-bar planar parallel mechanism show the effectiveness and competitiveness of the proposed control.
Similar content being viewed by others
Abbreviations
- AI:
-
artificial intelligence
- AMFFMC:
-
accurate model-free fuzzy motion control
- ANFIS:
-
adaptive network-based fuzzy inference system
- FIS:
-
fuzzy inference system
- FL:
-
fuzzy logic
- FLC:
-
fuzzy logic control
- FLS:
-
fuzzy logic system
- fuzzy P + ID:
-
fuzzy logic proportional plus conventional integral and derivative
- MF:
-
membership function
- MF-PID-FLC:
-
model-free PID fuzzy feed forward control
- MISO:
-
multi-input–single-output
- NN:
-
neural network
- PID:
-
proportional, integral, derivative
- RMSE:
-
root-mean-square-error
- TSK:
-
Takagi-Sugeno-Kang
References
Akbas, K.: Application of Neural Networks to Modeling and Control of Parallel Manipulators. In: Ryu, J. H. (ed.) Parallel Manipulators, New Developments, pp. 21–40. I-Tech Education and Publishing, Vienna, Austria (2008)
AL-Saedi, M., Wu, H., Handroos, H.: ANFIS And fuzzy tuning of PID controller for trajectory tracking of a flexible hydraulically driven parallel robot machine. Journal of Automation and Control Engineering 1(3), 70–77 (2013)
Astrom, K.J., Hang, C.C., Persson, P., Ho, W.K.: Towards intelligent PID control. Automatica 28(1), 1–9 (1992)
Chang, P., Jung, P.: A systematic method for gain selection of robust PID control for nonlinear plants of secondorder controller canonical form. IEEE Trans. Control Syst. Technol. 17, 473–483 (2009)
Chen, W.H., Yang, J., Guo, L., Li, S.: Disturbance-observer-based control and related methods - an overview. IEEE Trans. Ind. Electron. 63(2), 1083–1095 (2016)
Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal on International Fuzzy Systems 2, 267–278 (1994)
Demirli, K., Cheng, S.X., Muthukumaran, P.: Subtractive clustering based modeling of job sequencing with parametric search. Fuzzy Sets Syst. 137(2), 235–270 (2003)
Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., Herrera, F.: A review of the application of multiobjective evolutionary fuzzy systems: current status and further directions. IEEE Trans. Fuzzy Syst. 21(1), 46–65 (2013)
Feng, G.: A survey on analysis and design of model-based fuzzy control systems. IEEE Trans. Fuzzy Syst. 14(5), 676–697 (2006)
Fliess, M., Join, C.: Model-free control. Int. J. Control. 86(12), 2228–2252 (2013)
Formentin, S., de Filippi, P., Corno, M., Tanelli, M., Savaresi, S.: Data-driven design of braking control systems. IEEE Trans. Control Syst. Technol. 21, 186–193 (2013)
Ho, W. K., Hong, Y, Hansson, A, Hjalmarsson, H., Deng, J.W.: Relay auto-tuning of PID controllers using iterative feedback tuning. Automatica 39(1), 149–157 (2003)
Jang, J.-S. R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Keel, L.H., Bhattacharya, S.P.: Controller synthesis free of analytical models: three term controllers. IEEE Trans. Autom. Control 53, 1353–1369 (2008)
Li, W.: Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller. IEEE Trans. Fuzzy Syst. 6(4), 449–463 (1998)
Lin, C.M., Li, H.Y.: Dynamic petri fuzzy cerebellar model articulation controller design for a magnetic levitation system and a two-axis linear piezoelectric ceramic motor drive system. IEEE Trans. Control Syst. Technol. 23(2), 693–699 (2015)
Mahfouf, M., Abbod, M.F., Linkens, D.A.: Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control. IEEE Trans. Syst. Man Cybern. B Cybern. 33(3), 465–475 (2003)
Mamdani, E.H., Assilian, S.: Applications of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Elec. Eng. 121, 1585–1588 (1974)
Mendel, J., Hagras, H., Tan, W., Melek, W., Ying, H.: Introduction to type-2 fuzzy logic control, theory and applications. Wiley-IEEE Press (2014)
Milanes, V., Villagra J., Perez, J., Gonzalez C.: Low speed longitudinal controllers for mass-produced cars: a comparative study. IEEE Trans. Ind. Electron. 59, 620–628 (2012)
Morales, R., Feliu, V., Sira-Ramirez, H.: Nonlinear control for magnetic levitation systems based on fast online algebraic identification of the input gain. IEEE Trans. Control Syst. Technol. 19, 757–77 (2011)
Pedrycz, W.: Identification in fuzzy systems. IEEE Trans. Fuzzy Syst. 14, 361–366 (1984)
Precup, R.E., Hellendo, H.: A survey on industrial applications of fuzzy control. Comput. Ind. 62(3), 213–226 (2011)
Qi, Z. Mcinroy J.E., Jafari, F.: Trajectory tracking with parallel robots using low chattering fuzzy sliding mode controller. J. Intell. Robot. Syst. 48(3), 333–356 (2007)
Rad, A.B., Chan, P.T., Mok, C.K.: An online learning fuzzy controller. IEEE Trans. Ind. Electron. 50(5), 1016–1021 (2003)
Ren, Q., Baron, L., Balazinski, M., Jemielniak, K.: TSK Fuzzy modeling for tool wear condition in turning processes: an experimental study. Eng. Appl. Artif. Intel. 24(2), 260–265 (2011)
Ren, Q., Balazinski, M., Jemielniak, K., Baron, L., Achiche, S.: Experimental and fuzzy modeling analysis on dynamic cutting force in micro-milling. Soft. Comput. 17, 1687–1697 (2013)
Ren, Q., Bigras, P.: Model-free adaptive neural fuzzy feed forward torque control for nonlinear parallel mechanism. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Busan, Korea, 1043–1048 (2015)
Ren, Q., Baron, L., Balazinski, M., Jemielniak, K., Botez, R., Achiche, S.: Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling. Inf. Sci. 255, 121–134 (2014)
Sugeno, M., Kang, G.: Fuzzy modeling and control of multilayer incinerator. Fuzzy Set. Syst. 18, 329–346 (1986)
Sun, Y.L., Er, M.J.: Hybrid fuzzy control of robotics systems. IEEE Trans. Fuzzy Syst. 12(6), 755–765 (2004)
Syafiie, S., Tadeo, F., Martinez, E., Alvarez, T.: Model free control based on reinforcement learning for a wastewater treatment problem. Appl. Soft Comput. 1, 73–82 (2011)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)
Tong, R.M. Gupta, M.M., Rewgade, R.K., Yager, R.R. (eds.): The construction and evaluations of fuzzy models. North-Holland, Amsterdam (1979)
Villagra, J., Herrero-Perez, D.: A comparison of control techniques for robust docking maneuvers for an AVG. IEEE Trans. Control Syst. Technol. 20, 1116–1123 (2012)
Wang, F.Y., Liu, D.: Networked Control Systems: Theory and Applications. Springer (2008)
Yager, R.R., Filev, P.: Essentials of Fuzzy Modeling and Control. Wiley, New York (1994)
Qu, Z., Dawson, D.M.: Robust Tracking Control of Robot Manipulators, p 233. IEEE Press (1996)
Zi, B.Y., Duan, J., Du, L., Bao, H.: Dynamic modeling and active control of a cable-suspended parallel robot. Mechatronics 18(1), 1–12 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ren, Q., Bigras, P. A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller. J Intell Robot Syst 86, 367–379 (2017). https://doi.org/10.1007/s10846-016-0448-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-016-0448-7