A Novel adaptive neuro-fuzzy control scheme for eliminating rule explosion in dynamic systems
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Abstract
This article presents a neuro-fuzzy adaptive control scheme for avoiding the problem of rule explosion inherently associated with fuzzy control systems. The study proposes a novel neuro-fuzzy model designed using only two membership functions and four if-then fuzzy rules for control of highly nonlinear two-stage cart and pendulum system. The neuro-fuzzy controller has been designed with the minimum possible number of rules thus solving the problem of rule explosion completely. The study further compares proposed ANFIS controller with conventional proportional-integral-derivative (PID) and artificial neural network controllers in terms of settling time, overshoot ranges and steady state error. The results show better performance of ANFIS controller compared to PID and neural controllers.
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References
S. Shreedharan, V. Ravikumar and S.K. Mahadevan, “Design and control of real-time inverted pendulum system with force-voltage parameter correlation,” International Journal of Dynamics and Control, Vol. 9, No. 3, pp. 1672-1680, 2021.
L.B. Prasad, B. Tyagi and H.O. Gupta, “Optimal control of nonlinear inverted pendulum system using PID controller and LQR: Performance analysis without and with disturbance input,” International Journal of Automation and Computing, Vol. 11, pp. 661-670, 2014.
T. Johnson, S. Zhou, W. Cheah, W. Mansell, R. Young and S. Watson, “Implementation of a perceptual controller for an inverted pendulum robot,” Journal of Intelligent & Robotic Systems, Vol. 99, pp. 683-692, 2020.
B. Li, X. Rui and Q. Zhou, “Study on simulation and experiment of control of multiple launch rocket system by computed torque method,” Nonlinear Dynamics, Vol. 91, No.3, pp. 1-14, 2018.
W. Fu, B. Yan, X. Chang and J. Yan, “Guidance law and neural control for hypersonic missile to track targets,” Discrete Dynamics in Nature and Society, Vol. 2016, pp. 1-10, 2016.
M.H. Chiang and F.R. Chang, “Anthropomorphic design of the human-like walking robot,” Journal of Bionic Engineering, Vol. 10, pp. 186-193, 2013.
H. Yun, J. Bang, J. Kim and J. Lee, “High speed segway control with elastic actuator for driving stability improvement,” Journal of Mechanical Science and Technology, Vol. 33, pp. 5449-5459, 2019.
O.A. Votrina, K.N. Meleshkin and G.A. Frantsuzova, “On the problem of synthesis of the controller based on sliding modes for a model object in the shape of a double inverted pendulum on a cart,” Optoelectronics, Instrumentation and Data Processing, Vol. 57, pp. 356-362, 2021.
X. Chen, R. Yu, K. Huang, S. Zhen, H. Sun and K. Shao, “Linear motor driven double inverted pendulum: A novel mechanical design as a testbed for control algorithms,” Simulation Modelling Practice and Theory, Vol. 81, pp. 31-50, 2018.
M. Giacomelli, F. Padula, L. Simoni and A. Visioli, “Simplified input-output inversion control of a double pendulum overhead crane for residual oscillation reduction,” Mechatronics, Vol. 56, pp. 37-47, 2018.
I.M. Mehedi, U.M. Al-Saggaf, R. Mansouri and M. Bettayeb, “Stabilization of a double inverted rotary pendulum through fractional order integral control scheme,” International Journal of Advanced Robotic Systems, Vol. 16, No. 4, pp. 1-9, 2019.
R. Kumar, S. Gupta and S.F. Ali, “Energy harvesting from chaos in base excited double pendulum,” Mechanical Systems and Signal Processing, Vol. 124, pp. 49-64, 2019.
Z.B. Hazem, M.J. Fotuhi and Z. Bingul, “Development of a fuzzy-LQR and Fuzzy-LQG stability control for a double link rotary inverted pendulum,” Journal of Franklin Institute, Vol. 357, No.15, pp. 10529-10556, 2020.
S.D.A Sanjeewa and M. Parnichkun, “Control of rotary double inverted pendulum system using LQR sliding surface based sliding mode controller,” Journal of Control and Decision, Vol. 9, No.1, pp. 89-101, 2022.
K. Andrezejewski, M. Czyzniewski, M. Zielonka, R. Langowski and T. Zubowicz, “A comprehensive approach to double inverted pendulum modeling,” Archives of Control Sciences, Vol. 29, No.3, pp. 459-483, 2019.
J.J. Wang, “Simulation studies of inverted pendulum based on PID controllers,” Simulation Modeling Practice and Theory, Vol. 19, No.1, pp. 440-449, 2011.
G. Zhang, X. Gong and X. Chen, “PID control algorithm based on genetic algorithm and its application in electric cylinder control,” International Journal of Information Technology and Web Engineering, Vol. 12, No.3, pp. 51-61, 2017.
K. Borgeest and P.J. Schneider, “PID, fuzzy and model predictive control applied to a practical nonlinear plant,” International Journal of Robotics Application and Technologies, Vol. 4, No.1, pp. 19-42, 2016.
B.F. Barreiros, J.O. Trierweiler and M. Farenzena, “Reliable and straightforward PID tuning rules for highly underdamped systems,” Brazilian Journal of Chemical Engineering, Vol. 38, pp. 731-745, 2021.
K.K. Natarajan and J. Gokulachandran, “Application of artificial neural network techniques in computer aided process planning-a review,” International Journal of Process Management and Benchmarking, Vol. 11, No.1, pp. 80-100, 2021.
A.B. Badiru, “Quality insights: artificial neural network and taxonomical analysis of activity networks in quality engineering,” International Journal of Quality Engineering and Technology, Vol. 7, No. 2, pp. 99-107, 2018.
A. Al-Mayyahi, W. Wang and P. Birch, “Levenberg-Marquardt optimized neural networks for trajectory tracking of autonomous ground vehicles,” International Journal of Mechatronics and Automation, Vol. 5(2/3), pp. 140-153, 2015.
Y. Ramakrishna, P.V. Subbaiah and B.P. Rao, “A novel hybrid adaptive algorithm for improvement of mean square error convergence,” International Journal of Systems, Control and Communications, Vol. 6, No.1, pp. 59-68, 2014.
D. Susitra and S. Paramasivan, “Artificial intelligence-based rotor position estimation for 6/4 pole switched reluctance machine from phase inductance,” International Journal of Modeling, Identification and Control, Vol. 22, No.1, pp. 68-79, 2014.
B. Selma, S. Chouraqui and H. Abouaissa, “Hybrid ANFIS-ant colony based optimization for quadrotor trajectory tracking control,” International Journal of Modeling, Identification and Control, Vol. 34, No.1, pp. 13-25, 2020.
A. Kharola and P. Patil, “Ball and beam system control using PID-based ANFIS controllers,” International Journal of Advanced Mechatronic Systems, Vol. 7, No.1, pp. 24-34, 2016.
D.O. Araromi, O.O. Ajala and A.B. Sulayman, “Design of fuzzy-based adaptive controllers for filtration process,” International Journal of Nonlinear Dynamics and Control, Vol. 1, No.2, pp. 171-188, 2018.
C. Naresh, P.S.C. Bose and C.S.P. Rao, “Artificial neural networks and adaptive neuro-fuzzy models for predicting WEDM machining responses of Nitinol alloy: comparative study,” SN Applied Science, Vol. 2, pp. 7-23, 2020.
M. Babanezhad, A. Masoumian, A.T. Nakhjiri, A. Marjani and S. Shirazian, “Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system (ANFIS),” Scientific Reports, Vol. 10, pp. 1-20, 2020.