Robust Adaptive Tracking Control of Manipulator Arms with Fuzzy Neural Networks
The learning space for executing general motions of a flexible joint manipulator is quite large and the dynamics are, in general, nonlinear, time-varying, and complex. The objective of this paper is to design a nonlinear system based on the fuzzy neural network control using supervised training, into executing reference trajectories by a flexible joint manipulator. The structure identifications of controller networks are performed by using the Adaptive Neural Fuzzy Inference System (ANFIS), with new parameters and weight coefficients automatically adapted and adjusted, in order to decrease position tracking errors. In order to adapt and reduce the number of undefined parameters in the network, a new technique is used. Reported research works use the Euler method for the resolution of the arm's dynamic function, in this paper, a more exact method was used, represented by the Fourth-Order Runge-Kutta (RK4) method. A comparative study has been carried out between these two methods in order to prove the effectiveness of the later. Finally, in order to test the robustness of the proposed approach, it was also investigated considering parameter variations. The tracking speed of the model on the system control accuracy was also analyzed. The simulation results show that the proposed approach has a good tracking effect.
J. Iqbal, M. Ul Islam, S. Abbas, A. A. Khan, and S. Ajwad, “Automating industrial tasks through mechatronic systems – A review of robotics in industrial perspective,” Tehnicki Vjesnik, vol. 23, no. 3, pp. 917–924, Jun. 2016, doi: 10.17559/TV-20140724220401.
J. Wawerla and R. T. Vaughan, “A fast and frugal method for team-task allocation in a multi-robot transportation system,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, May 2010, pp. 1432–1437, doi: 10.1109/ROBOT.2010.5509865.
Mitsuru Endo et al., “A car transportation system by multiple mobile robots - iCART -,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, Sep. 2008, pp. 2795–2801, doi: 10.1109/IROS.2008.4651200.
M. Fei, Z. Haiou, and W. Guilan, “Application of industrial robot in rapid prototype manufacturing technology,” in 2nd International Conference on Industrial Mechatronics and Automation, Wuhan, China, May 2010, vol. 1, pp. 218–220, doi: 10.1109/ICINDMA.2010.5538187.
L. Yanping and L. Haijiang, “Welding multi-robot task allocation for BIW based on hill climbing genetic algorithm,” in International Technology and Innovation Conference, Xi’an, China, Oct. 2009, pp. 1–8, doi: 10.1049/cp.2009.1485.
H. B. Chen, T. Lin, S. B. Chen, J. F. Wang, J. Q. Jia, and H. Zhang, “Adaptive control on wire feeding in robot arc welding system,” in IEEE Conference on Robotics, Automation and Mechatronics, Chengdu, China, Sep. 2008, pp. 119–122, doi: 10.1109/RAMECH.2008.4690868.
S. A. Ajwad, J. Iqbal, M. I. Ullah, and R. U. Islam, Modeling Robotic Arms – A Review and Derivation of Screw Theory Based Kinematics. Islamabad, Pakistan: Institute of Imformation Technology, 2014.
W. Alam, S. Ahmad, A. Mehmood, and J. Iqbal, “Robust Sliding Mode Control for Flexible Joint Robotic Manipulator via Disturbance Observer,” Interdisciplinary Description of Complex Systems, vol. 17, no. 1-B, pp. 85–97, 2019.
S. A. Ajwad, M. I. Ullah, K. Baizid, and J. Iqbal, “A comprehensive state-of-the-art on control of industrial articulated robots,” Journal of the Balkan Tribological Association, vol. 20, no. 4, pp. 499–521, Dec. 2014.
S. Ajwad, J. Iqbal, A. A. Khan, and A. Mehmood, “Disturbance-Observer-Based Robust Control of a Serial-link Robotic Manipulator Using SMC and PBC Techniques,” Studies in Informatics and Control, vol. 24, no. 4, pp. 401–408, Dec. 2015, doi: 10.24846/v24i4y201504.
S. Ajwad, J. Iqbal, M. U. Islam, A. Alsheikhy, A. Almeshal, and A. Mehmood, “Optimal and Robust Control of Multi DOF Robotic Manipulator: Design and Hardware Realization,” Cybernetics and Systems, vol. 49, no. 1, pp. 77–93, Feb. 2018, doi: 10.1080/01969722.2017.1412905.
Z. S. Awan, K. Ali, J. Iqbal, and A. Mehmood, “Adaptive Backstepping Based Sensor and Actuator Fault Tolerant Control of a Manipulator,” Journal of Electrical Engineering & Technology, vol. 14, no. 6, pp. 2497–2504, Nov. 2019, doi: 10.1007/s42835-019-00277-9.
J. Iqbal, “Modern Control Laws for an Articulated Robotic Arm: Modeling and Simulation,” Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 4057–4061, Apr. 2019.
W. Alam, A. Mehmood, K. Ali, U. Javaid, S. Alharbi, and J. Iqbal, “Nonlinear Control of a Flexible Joint Robotic Manipulator with Experimental Validation,” Journal of Mechanical Engineering, vol. 64, no. 1, pp. 47–55, Jan. 2018, doi: 10.5545/sv-jme.2017.4786.
J. Iqbal, M. Ullah, A. A. Khan, and M. Irfan, “Towards Sophisticated Control of Robotic Manipulators: An Experimental Study on a Pseudo-Industrial Arm,” Journal of Mechanical Engineering, vol. 61, no. 7, pp. 465–470, Jul. 2015, doi: 10.5545/sv-jme.2015.2511.
A. Ailon, R. Lozano, and M. I. Gil’, “Iterative regulation of an electrically driven flexible-joint robot with model uncertainty,” IEEE Transactions on Robotics and Automation, vol. 16, no. 6, pp. 863–870, Dec. 2000, doi: 10.1109/70.897798.
A. Benzaouia and A. El Hajjaji, Advanced Takagi‒Sugeno Fuzzy Systems, vol. 8. New York, NY, USA: Springer, 2014.
O. F. Lutfy, “Wavelet Neural Network Model Reference Adaptive Control Trained by a Modified Artificial Immune Algorithm to Control Nonlinear Systems,” Arabian Journal for Science and Engineering, vol. 39, no. 6, pp. 4737–4751, Jun. 2014, doi: 10.1007/s13369-014-1088-5.
G. Dreyfus et al., Reseaux de neurones, methodologie et application. Paris, France: Eyrolles, 2002.
M. Alizadeh, F. Jolai, M. Aminnayeri, and R. Rada, “Comparison of different input selection algorithms in neuro-fuzzy modeling,” Expert Systems with Applications, vol. 39, no. 1, pp. 1536–1544, Jan. 2012, doi: 10.1016/j.eswa.2011.08.049.
A. Subasi, “Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction,” Computers in biology and medicine, vol. 37, no. 2, pp. 227–244, Mar. 2007, doi: 10.1016/j.compbiomed.2005.12.003.
M. R. U. Islam, J. Iqbal, and Q. Khan, “Design and Comparison of Two Control Strategies for Multi-DOF Articulated Robotic Arm Manipulator,” Control Engineering and Applied Informatics, vol. 16, no. 2, pp. 28–39, Jun. 2014.
S. A. Ajwad, J. Iqbal, M. I. Ullah, and A. Mehmood, “A systematic review of current and emergent manipulator control approaches,” Frontiers of Mechanical Engineering, vol. 10, no. 2, pp. 198–210, Jun. 2015, doi: 10.1007/s11465-015-0335-0.
W. T. Miller, F. H. Glanz, and L. G. Kraft, “Application of a General Learning Algorithm to the Control of Robotic Manipulators,” The International Journal of Robotics Research, vol. 6, no. 2, pp. 84–98, Jun. 1987, doi: 10.1177/027836498700600207.
H. Taheri Shahraiyni, S. Sodoudi, A. Kerschbaumer, and U. Cubasch, “A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas,” Engineering Applications of Artificial Intelligence, vol. 41, pp. 175–182, May 2015, doi: 10.1016/j.engappai.2015.02.010.
M. Panella, A. Rizzi, F. M. F. Mascioli, and G. Martinelli, “ANFIS synthesis by hyperplane clustering,” in 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada, Jul. 2001, vol. 1, pp. 340–345, doi: 10.1109/NAFIPS.2001.944275.
M. Dong and N. Wang, “Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness,” Applied Mathematical Modelling, vol. 35, no. 3, pp. 1024–1035, Mar. 2011, doi: 10.1016/j.apm.2010.07.048.
J. F. Joduin, Les reseaux de neurones. Principe et definitions. Paris, France: Hermes, 1994.
D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications. Cambridge, USA: Academic Press, 1980.
M. M. A. Elsalam, S. F. Sarayah, and F. M. Banoor, “Fuzzy Control of Multi-Link Robotic Arm with Multi-Weight Objects,” Current Science International, vol. 6, no. 1, pp. 218–228, 2017.
P. Deka and V. Chandramouli, “A fuzzy neural network model for deriving the river stage—discharge relationship,” Hydrological Sciences Journal, vol. 48, no. 2, pp. 197–209, Apr. 2003, doi: 10.1623/hysj.126.96.36.199697.
A. Tang, C. Quek, and G. Ng, “GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms,” Expert Systems with Applications, vol. 29, no. 4, pp. 769–781, Nov. 2005, doi: 10.1016/j.eswa.2005.06.001.
W.-H. Ho, J.-T. Tsai, B.-T. Lin, and J.-H. Chou, “Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm,” Expert Systems with Applications, vol. 36, no. 2, pp. 3216–3222, Mar. 2009, doi: 10.1016/j.eswa.2008.01.051.
T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116–132, Feb. 1985, doi: 10.1109/TSMC.1985.6313399.
H. R. Berenji and P. Khedkar, “Learning and tuning fuzzy logic controllers through reinforcements,” IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 724–740, Sep. 1992, doi: 10.1109/72.159061.
H. K. Lam and S. C. Tan, “Stability analysis of fuzzy-model-based control systems: application on regulation of switching DC-DC converter,” IET Control Theory & Applications, vol. 3, no. 8, pp. 1093–1106, Aug. 2009, doi: 10.1049/iet-cta.2008.0168.
H. K. Lam and L. D. Seneviratne, “Tracking control of sampled-data fuzzy-model-based control systems,” IET Control Theory & Applications, vol. 3, no. 1, pp. 56–67, Jan. 2009, doi: 10.1049/iet-cta:20070466.
L. Xiong, A. Y. Shamseldin, and K. M. O’Connor, “A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system,” Journal of Hydrology, vol. 245, no. 1, pp. 196–217, May 2001, doi: 10.1016/S0022-1694(01)00349-3.
C. C. Lee, “Fuzzy logic in control systems: fuzzy logic controller. I,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, no. 2, pp. 404–418, Apr. 1990, doi: 10.1109/21.52551.
M. Samhouri, A. Al-Ghandoor, S. A. Ali, I. Hinti, and W. Massad, “An Intelligent Machine Condition Monitoring System Using Time-Based Analysis: Neuro-Fuzzy Versus Neural Network,” Jordan Journal of Mechanical and Industrial Engineering, vol. 3, no. 4, pp. 294–305, Dec. 2009.
B. S. Reddy, J. S. Kumar, and K. V. K. Reddy, “Prediction of Surface Roughness in Turning Using Adaptive Neuro-Fuzzy Inference System,” Jordan Journal of Mechanical and Industrial Engineering, vol. 3, no. 4, pp. 252–259, Dec. 2009.
Y. Shi and M. Mizumoto, “Some considerations on conventional neuro-fuzzy learning algorithms by gradient descent method,” Fuzzy Sets and Systems, vol. 112, no. 1, pp. 51–63, May 2000, doi: 10.1016/S0165-0114(98)00056-6.
W. Afzal, S. Iqbal, Z. Tahira, and M. E. Qureshi, “Gesture Control Robotic Arm Using Flex Sensor,” Applied and Computational Mathematics, vol. 6, no. 4, pp. 171–176, Jan. 2017, doi: 10.11648/j.acm.20170604.12.
C. Chavez-Olivares, F. Reyes Cortes, and E. Gonzalez-Galvan, “On Explicit Force Regulation with Active Velocity Damping for Robot Manipulators,” Automatika, vol. 56, no. 4, pp. 478–490, Jan. 2015, doi: 10.7305/automatika.2016.01.399.
J. Butcher, Runge–Kutta methods for ordinary differential equations. Wellington, New Zealand: The University of Auckland, 2005.
B. Stout, Methodes numeriques de resolution d’equations differentielles. Marseille, France: Universite de Provence, 2007.
E. G. Da Silva, Methodes et Analyse Numeriques. Grenoble, France: Institut Polytechnique de Grenoble, 2007.
S. Ullah, A. Mehmood, Q. Khan, S. Rehman, and J. Iqbal, “Robust Integral Sliding Mode Control Design for Stability Enhancement of Under-actuated Quadcopter,” International Journal of Control, Automation and Systems, vol. 18, no. 7, pp. 1671–1678, Jul. 2020, doi: 10.1007/s12555-019-0302-3.
Z. Civelek, “Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm,” Engineering Science and Technology, an International Journal, vol. 23, no. 1, pp. 1–9, Feb. 2020, doi: 10.1016/j.jestch.2019.04.010.
S. A. Ajwad, A. Mehmood, M. Ullah, and J. Iqbal, “Optimal V/S robust control: A study and comparision for articulated manipulator,” Journal of the Balkan Tribological Association, vol. 22, no. 3, pp. 2460–2466, 2016.
Y. Yanling, “Model Free Adaptive Control for Robotic Manipulator Trajectory Tracking,” The Open Automation and Control Systems Journal, vol. 7, no. 1, pp. 358–365, Apr. 2015, doi: 10.2174/1874444301507010358.
H. Seraji, “Decentralized adaptive control of manipulators: theory, simulation, and experimentation,” IEEE Transactions on Robotics and Automation, vol. 5, no. 2, pp. 183–201, Apr. 1989, doi: 10.1109/70.88039.
C. H. Wu, K. Y. Young, K. S. Hwang, and S. Lehman, “Voluntary movements for robotic control,” IEEE Control Systems Magazine, vol. 12, no. 1, pp. 8–14, Feb. 1992, doi: 10.1109/37.120444.
M. B. Ayed, L. Zouari, and M. Abid, “Software In the Loop Simulation for Robot Manipulators,” Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2017–2021, Oct. 2017.
MetricsAbstract Views: 61
PDF Downloads: 58
Copyright (c) 2020 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.