Adaptive dynamic surface control of a flexible-joint robot with parametric uncertainties

Document Type : Article

Authors

College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Abstract

A new kind of adaptive dynamic surface control (DSC) method is proposed to overcome parametric uncertainties of flexible-joint (FJ) robots. These uncertainties of FJ robots are transformed into linear expressions of inertial parameters which are estimated based on the DSC, and the high-order derivatives in DSC are solved by using first-order filter. The adaptation laws of inertial parameters are designed directly to improve the tracking performance according to the Lyapunov stability analysis. Simulation results for a two-link FJ robot show the better tracking accuracy against model parametric uncertainties. The method used does not need aid of Neural Network (NN), and is simpler and calculation faster than the other adaptive methods

Keywords

Main Subjects


Refrences:
1.Chen, Z.Y. and Chen, L. Adaptive backstepping control of exible-joint space robot based on neural network", Engineering Mechanics, 30(4), pp. 397-401 (2013).
2. Macnab, C.J.B. and D'Eleuterio, G.M.T. Neuroadaptive control of elastic-joint robots using robust performance enhancement", Robotica, 19(6), pp. 619-629 (2001).
3. Nanos, K. and Papadopoulos, E. On the use of free-oating space robots in the presence of angular momentum", Intelligent Service Robotics, 4(1), pp. 3- 15 (2011). 2758 C.G. Li et al./Scientia Iranica, Transactions B: Mechanical Engineering 26 (2019) 2749{2759
4. Karabacak, M. and Eskikurt, H.I. Design, modelling and simulation of a new nonlinear and full adaptive backstepping speed tracking controller for uncertain PMSM", Applied Mathematical Modelling, 36(11), pp. 5199-5213 (2012). 5. Karabacak, M. and Eskikurt, H.I. Speed and current regulation of a permanent magnet synchronous motor via nonlinear and adaptive backstepping control", Mathematical & Computer Modelling, 53(9-10), pp. 2015-2030 (2011). 6. Swaroop, D., Hedrick, J.K., Yip, P.P., and Gerdes, J.C. Dynamic surface control for a class of nonlinear systems", IEEE Transactions on Automatic Control, 45(10), pp. 1893-1899 (2002). 7. Wu, Z.H., Lu, J.C., and Shi, J.P. Adaptive neural dynamic surface control of morphing aircraft with input constraints", 29th Chinese Control and Decision Conference, Chongqing, China, pp. 6-12 (2017). 8. Zhang, T., Xia, M., Yi, Y., and Shen, Q. Adaptive neural dynamic surface control of pure-feedback nonlinear systems with full state constraints and dynamic uncertainties", IEEE Transactions on Systems Man Cybernetics-Systems, 47(8), pp. 2378-2387 (2017). 9. Wang, C., Wu, Y., and Yu, J. Barrier Lyapunov functions-based dynamic surface control for purefeedback systems with full state constraints", IET Control Theory and Applications, 11(4), pp. 524-530 (2017). 10. Shin, J. Adaptive dynamic surface control for a hypersonic aircraft using neural networks", IEEE Transactions on Aerospace and Electronic Systems, 53(5), pp. 2277-2289 (2017). 11. Gao, S.G., Dong, H.R., Ning, B., Tang, T., Li, Y.D., and Valavanis, K.P. Neural adaptive dynamic surface control for mismatched uncertain nonlinear systems with nonlinear feedback errors", 36th Chinese Control Conference (CCC). Dalian, China, pp. 828-833 (2017). 12. Min, W. and Huiping, Y. Adaptive neural dynamic surface control for exible joint manipulator with prescribed performance", 29th Chinese Control and Decision Conference, Chongqing, China, pp. 5311- 5316 (2017). 13. Uyen, H.T.T., Tuan, P.D., Tu, V.V., Quang, L., and Minh, P.X. Adaptive neural networks dynamic surface control algorithm for 3 DOF surface ship", International Conference on System Science and Engineering, Ho Chi Minh City, Vietnam, pp. 71-76 (2017). 14. Li, C., Cui, W., You, J., Lin, J., and Xie, Z. Neural network adaptive backstepping control of multi-link exible-joint robots", Journal of Shanghai Jiaotong University, 50(7), pp. 1095-1101 (2016) (In Chinese). 15. Zhai, D., Xi, C., An, L., Dong, J., and Zhang, Q. Prescribed performance switched adaptive dynamic surface control of switched nonlinear systems with average dwell time", IEEE Transactions on Systems Man Cybernetics-Systems, 47(7), pp. 1257-1269 (2017). 16. Su, H. and Zhang, W. A combined backstepping and dynamic surface control to adaptive fuzzy statefeedback control", International Journal of Adaptive Control and Signal Processing, 31(11), pp. 1666-1685 (2017). 17. Gao, S., Dong, H., Ning, B., and Yao, X. Singleparameter- learning-based fuzzy fault-tolerant output feedback dynamic surface control of constrained-input nonlinear systems", Information Sciences, 385, pp. 378-394 (2017). 18. Edalati, L., Edalati, L., Sedigh, A.K., Shooredeli, M.A., and Moare_anpour, A. Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints", Mechanical Systems and Signal Processing, 100, pp. 311- 329 (2018). 19. Rahmani, S. and Shahriari-kahkeshi, M. Adaptive dynamic surface control design for a class of uncertain nonlinear systems using interval type-2 fuzzy systems", Iranian Conference on Electrical Engineering, Tehran, Iran, pp. 817-822 (2017). 20. Liu, X., Su, C.Y., and Yang, F. FNN approximationbased active dynamic surface control for suppressing chatter in micro-milling with piezo-actuators", IEEE Transactions on Systems Man Cybernetics-Systems, 47(8), pp. 2100-2113 (2017). 21. Zhu, Q., Ma, J., Liu, Z., and Liu, K. Containment control of autonomous surface vehicles: A nonlinear disturbance observer-based dynamic surface control design", Advances in Mechanical Engineering, 9(10), pp. 1-13 (2017). 22. Zhou, C., Zhu, J., Lei, H., and Yuan, X. Observerbased dynamic surface control for high-performance aircraft subjected to unsteady aerodynamics and actuator saturation", Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, 231(6), pp. 481-494 (2017). 23. Dian, S., Chen, L., Son, H., Zhao, T., and Tan, J. Gain scheduled dynamic surface control for a class of underactuated mechanical systems using neural network disturbance observer", Neurocomputing, 275, pp. 1998-2008 (2018). 24. Yu, Z., Qu, Y., and Zhang, Y. Robust adaptive dynamic surface control for receiver UAV during boom refueling in the presence of vortex", 29th Chinese Control and Decision Conference, Chongqing, China, pp. 1798-1803 (2017). 25. Li, X., Wang L., and Sun, Y. Dynamic surface backstepping sliding mode position control of permanent magnet linear synchronous motor", IEEE International Electric Machines and Drives Conference, Miami, FL, USA, pp. 1-7 (2017). 26. Chen, Z., Lin, Z., Huang, G., Jia, H., and Yue, C. Particle swarm optimized adaptive dynamic surface control for PMSM servo system", 36th Chinese Control Conference (CCC), Dalian, China, pp. 4751-4756 (2017). C.G. Li et al./Scientia Iranica, Transactions B: Mechanical Engineering 26 (2019) 2749{2759 2759 27. Liang, T., Wang, W., Wu, S., and Lu, K. Nonlinear attitude control of tiltrotor aircraft based on dynamic surface adaptive backstepping", 29th Chinese Control and Decision Conference, Chongqing, China, pp. 603- 608 (2017). 28. Yoo, S.J., Jin, B.P., and Choi, Y.H. Adaptive output feedback control of exible-joint robots using neural networks: dynamic surface design approach", IEEE Trans Neural Networks, 19(10), pp. 1712-1726 (2008). 29. Ortega, R. and Spong, M.W. Adaptive motion control of rigid robots: a tutorial", Automatica, 25(6), pp. 877-888 (1989).