The motion control of single link flexible joint robot manipulator is reduce the vibration of tip of the manipulator. The advance methodology is to optimize fuzzy logic controller parameters via neural network and use the adaptive neuro-fuzzy scheme to control single link flexible joint robot manipulators. The dynamics of single link robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. The increased complexity of the motions of robots manipulator considering joint elasticity makes conventional control strategies complex and difficult to synthesize. This paper presents investigations into the development of adaptive neuro Fuzzy control for position and velocity control of a flexible joint manipulator. To study the effectiveness of the controllers, a adaptive Neuro Fuzzy Controller is developed for tip angular position and velocity control of a single link flexible joint manipulator. This is then extended to incorporate an ANFIS Controller for velocity error reduction of the flexible joint system. Simulation results of the response of the flexible joint manipulator with the ANFIS controllers are presented in time domains. The performances of the ANFIS schemes are examined in terms of input position and velocity tracking capability, level of robot vibration reduction and time response specifications.

Keywords: Flexible structure, Manipulator, ANFIS, Uncertain system.

Zhijun  Li; Quanbo  Ge; Wenjun  Ye; Peijiang  Yuan “Dynamic Balance Optimization and Control of Quadruped Robot Systems With Flexible Joints” IEEE  Transactions  on  Systems,  Man,  and Cybernetics: Systems Year:  2016, Volume: 46, Issue: 10.

Michael  Ruderman  “Compensation  of  Nonlinear Torsion   in Flexible Joint Robots:   Comparison   of Two Approaches”  IEEE Transactions on Industrial Electronics Year: 2016, Volume: 63, Issue: 9.

Shuang  Song; Zheng  Li; Haoyong  Yu; Hongliang Ren  “Electromagnetic Positioning for Tip Tracking and Shape Sensing of FlexibleRobots” IEEE Sensors Journal Year: 2015, Volume: 15, Issue: 8.

Young Jin Park; Wan Kyun Chung  External torque- sensing  algorithm  for flexible-joint robot based  on Kalman      filter Electronics  Letters Year: 2013, Volume: 49, Issue: 14.

Chaio-Shiung Chen “Robust Self-Organizing Neural- Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems” IEEE Transactions on Fuzzy Systems Year: 2011, Volume: 19, Issue: 4.

Y.  Pan,  U.  Ozguner, and  O.  H.  Dagci,  “Variable structure control of electronic throttle valve,” IEEE Trans. Ind. Electron., vol. 55, no. 11, pp. 3899–3907, Nov. 2008.

Chatterjee, R. Chatterjee, F. Matsuno, and T. Endo, “Augmented stable fuzzy control for flexible robotic arm using LMI approach and neuro fuzzy state space modeling,” IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1256–1270, Mar. 2008.

L. Sweet and M. Good, “Redefinition of the robot motion control problem,” IEEE Control Syst. Mag., vol. 5, no. 3, pp. 18–25, Aug. 1985.

Armstrong and C. C. de Wit, “Friction modeling and compensation,” in The Control Handbook, vol. 77. Boca Raton, FL: CRC Press, 1996, pp. 1369–1382.

H. Olsson, K. Astrom, C. C. de Wit, M. Gafvert, and P. Lischinsky, “Friction models and friction compensation,” Eur. J.  Control, vol. 4, no.  3, pp.176–195, 1998.

S.  Katsura  and  K.  Ohnishi,  “Force  servoing  by flexible manipulator based on resonance ratio control,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 539–547, Feb. 2007.

Seidl, S. L. Lam, J. Putman, and R. Lorenz, “Neural network compensation of gear backlash hysteresis in position controlled mechanisms,” IEEE Trans. Ind. Appl.,  vol.  31,  no.  6,  pp.  1475–1483,  Nov./Dec.1995.

S. Katsura, J. Suzuki, and K. Ohnishi, “Pushing operation by flexible manipulator taking environmental   information   into   account,”   IEEE Trans. Ind. Electron., vol. 53, no. 5, pp. 1688–1697, Oct. 2006.

F.  Ghorbel,  J.  Hung,  and  M.  Spong,  “Adaptive control of flexible joint manipulators,” IEEE Control Syst. Mag., vol. 9, no. 7, pp. 9–13, Dec. 1989.

M. C. Chien and A. C. Huang, “Adaptive control for flexible joint electrically driven robot with time varying uncertainties,” IEEE Trans. Ind. Electron., vol. 54, no. 2, pp. 1032–1038, Apr. 2007.

Hace, K. Jezernik, and A. Sabanovic, “SMC with disturbance observer for a linear belt drive,” IEEE Trans. Ind. Electron., vol. 54, no. 6, pp. 3402–3412, Dec. 2007.

K.  Kyoungchul,  T.  Masayoshi,  M.  Hyosang,  H.Beomsoo, and J. Doyoung,   “Mechanical design and impedance compensation of SUBAR (Sogang University’s Biomedical Assist Robot),” in Proc. IEEE/ASME Int. Conf. Adv.   Intell. Mechatronics, Jul. 2008, pp. 377–382.

J. P. Hauschild and G. R. Heppler, “Control of harmonic drive motor actuated flexible linkages,” in Proc. IEEE Int. Conf. Robot. Autom., Apr. 2007, pp.3451–3456.

R.  Martinez,  J.  Alvarez,  and  Y.  Orlov,  “Hybrid sliding mode based control of underactuated systems with dry friction,” IEEE Trans. Ind. Electron., vol.55, no. 11, pp. 3998–4003, Nov. 2008.

F. J. Lin, Y. C. Hung, and S. Y. Chen, “FPGA based computed force control system using Elman neural network for  linear ultrasonic  motor,”  IEEE Trans. Ind. Electron., vol. 56, no. 4, pp. 1238–1253, Apr.2009.

Merabet and J. Gu, “Robust nonlinear predictive control with modeling uncertainties and unknown disturbance for single link flexible joint robot,” in Proc. 7th World Congr. Intell. Control Autom., Jun.2008, pp. 1516–1521.

F.A.   Alturkı,   A.   Abdennour,   “Design   and Simplification of  Adaptive  Neuro-Fuzzy Inference Controllers For Power Plants”, Electrical Power and Energy Systems, vol.21, pp.465-474, 1999.