MODEL-FREE ADAPTIVE CONTROL FOR TIME-VARYING TRAJECTORY TRACKING OF NON-LINEAR SYSTEMS

Ce Hao, Yueling Wang, Hongbin Wang, and Zhen Zhou

References

  1. [1] D. Braganza, W.E. Dixon, D.M. Dawson, and B. Xian, Tracking control for robot manipulators with kinematic and dynamic uncertainty, International Journal of Robotics and Automation, 23(2), 2008, 117–126.
  2. [2] F. Piltan, N. Sulaiman, A. Jalali, and F.D. Narouei, Design of model free adaptive fuzzy computed torque controller: Applied to nonlinear second order system, International Journal of Robotics and Automation, 2(4), 2011, 232–244.
  3. [3] X. Lu, B. Kiumarsi, T. Chai, and F.L. Lewis, Data-driven optimal control of operational indices for a class of industrial processes, IET Control Theory & Applications, 12(10), 2016, 1348–1356.
  4. [4] Z. Hou and S. Jin, A novel data-driven control approach for a class of discrete-time nonlinear systems, IEEE Transactions on control systems technology, 19, 2011, 1549–1558.
  5. [5] V. Santiba´nez, K. Camarillo, J. Moreno-Valenzuela, and R. Campa, A practical PID regulator with bounded torques for robot manipulators, International Journal of Control, Automation and Systems, 8(3), 2010, 544–555.
  6. [6] J.L. Meza, V. Santibáñez and R. Campa, An estimate of the domain of attraction for the PID regulator of manipulators, International Journal of Robotics and Automation, 22(22), 2007, 187–195.
  7. [7] J. Lee, W. Cho and T.F. Edgar, An improved technique for PID controller tuning from closed-loop tests, AICHE Journal, 36(12), 1990, 1891–1895.
  8. [8] D.T. Liem, D.Q. Truong and K.K. Ahn, A torque estimator using online tuning grey fuzzy PID for applications to torquesensorless control of DC motors, Mechatronics, 26, 2015, 45–63.
  9. [9] S. Azali and M. Sheikhan, Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzybased PID for maximum power point tracking, Applied Intelligence, 44(1), 2016, 88–110.
  10. [10] P. Jha and B.B. Biswal, A neural network approach for inverse kinematic of a SCARA manipulator, IAES International Journal of Robotics and Automation, 3(1), 2014, 52.
  11. [11] S. Li, L. Ding, H. Gao, C. Chen, Z. Liu, and Z. Deng, Adaptive neural network tracking control-based reinforcement learning for wheeled mobile robots with skidding and slipping, Neurocomputing, 283, 2017, 20–30.
  12. [12] A.A. Hussein, Capacity fade estimation in electric vehicle Li-ion batteries using artificial neural networks, IEEE Transactions on Industry Applications, 51(3), 2015, 2321–2330.
  13. [13] D. Chwa, Fuzzy adaptive output feedback tracking control of VTOL aircraft with uncertain input coupling and inputdependent disturbances, IEEE Transactions on Fuzzy Systems, 23(5), 2015, 1505–1518.
  14. [14] N. Hovakimyan, C. Cao, E. Kharisov, E. Xargay, and I.M. Gregory, Adaptive control for safety-critical systems, IEEE Control Systems, 31(5), 2011, 54–104.
  15. [15] F. Piltan, M.A. Bairami, F. Aghayari, and S. Allahdadi, Design adaptive artificial inverse dynamic controller: Design sliding mode fuzzy adaptive new inverse dynamic fuzzy controller, International Journal of Robotics and Automation, 3(3), 2012, 13–26.
  16. [16] R. Chi and Z. Hou, A model-free periodic adaptive control for freeway traffic density via ramp metering, Acta Automatica Sinica, 36(7), 2010, 1029–1033.
  17. [17] X. Wang, X. Li, J. Wang, X. Fang, and X. Zhu, Data-driven model-free adaptive sliding mode control for the multi degreeof-freedom robotic exoskeleton, Information Sciences, 327, 2016, 246–257.
  18. [18] Z. Hou and S. Jin, A novel data-driven control approach for a class of discrete-time nonlinear systems, IEEE Transactions on Control Systems Technology, 19, 2011, 1549–1558.
  19. [19] R. Chi and Z. Hou, A model-free periodic adaptive control for freeway traffic density via ramp metering, Acta Automatica Sinica, 36(7), 2010, 1029–1033.
  20. [20] B. Gao, Y. Chang, K. Gu, Y. Zeng, and Y. Liu, Physiological controller of an intra-aorta pump based on baroreflex sensitivity, Artificial Organs, 36(12), 2012, 1015–1025.

Important Links:

Go Back