NEURAL NETWORK WITH DYNAMIC PROGRAMMING FOR TIME-OPTIMAL CONTROL OF CONICAL TANK LEVEL

N.S. Bhuvaneswari,∗ G. Uma,∗∗ and T.R. Rangaswamy∗

References

  1. [1] R. Anandanatarajan, M. Chidambaram, & T. Jayasingh, Limitations of a PI controller for a first order non-linear process with dead time, ISA Transactions, 45, 2006, 185–200.
  2. [2] I.B. Lee & S.W. Sung, Limitations and countermeasures of PID controllers, Industrial and Engineering Chemistry, 35, 1996, 2596–2610.
  3. [3] J. Lan, J. Cho, D. Erdogmus, J.C. Principe, M.A. Motter, & J. Xu, Local linear PID controllers for nonlinear control, Control and Intelligent Systems, 33, 2005, 201–1541.
  4. [4] T.K. Madhubala, M. Boopathy, J.S. Chandra, & T.K. Radhakrishnan, Development and tuning of fuzzy controller for a conical level system, Proceedings of IEEE International Conference, ICISIP, 2004, 450–455.
  5. [5] N.S. Bhuvaneswari & P. Kanagasabapathy, Neural network with reinforcement learning for adaptive time optimal control of tank level, Modeling and Simulation, AMSE, 1, 2000.
  6. [6] J. Malmbarg & J. Eker, Hybrid control of a double tank system, Proceedings of IEEE International Conference on Control Applications, 1997, 133–138.
  7. [7] F. Borrelli, M. Baotic, A. Bemporad, & M. Morari, An efficient algorithm for computing the state feedback optimal control law for discrete time hybrid systems, Proc. of the American Control Conference, 2003, 4717–4722.
  8. [8] T.L. Song & S.J. Shin, Time optimal impact angle control for vertical plane engagements, IEEE Transactions on Aerospace and Electronic Systems, 35(2), 1999, 738–742.
  9. [9] N. Sakagami & S. Kawamura, Time optimal control for under water robot manipulators based on iterative learning control and time-scale transformation, IEEE Transactions on Automatic Control, 2003, 1180–1186.
  10. [10] J.E. Kulkarni, Time optimal control of a swing, Proc. 42nd IEEE Conf. on Decision and Control, Hawaii, USA, 2003.
  11. [11] G. Fang & G. Dissanayake, Time-optimal feedback control of a non-holonomic vehicle using neural networks, Seventh International Conference on Control, Automation, Robotics And Vision (ICARCV’OZ), Singapore, 2002, 1458–1463.
  12. [12] Y. Chen, T. Edgar, & V. Manousiouthakis, On infinite time non-linear quadratic optimal control Proc. 42nd Conf. on Decision and Control, Hawaii, USA, 2003, 221–226.
  13. [13] M. Reza Dehghan Nayeri, A. Alasty, & K. Daneshjou, Neural optimal control of flexible spacecraft slew maneuver, Acta Astronautica, 55, 2004, 817–827.
  14. [14] T.R. Rangaswamy, J. Shanmugam, & K.P. Mohammed, Adaptive fuzzy tuned PID controller for combustion of utility boilers, Control and Intelligent Systems, 33(1), 2005.
  15. [15] K.-S. Hwang et al., Reinforcement learning to adaptive control of nonlinear systems, IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, 33, 2003, 514–521.
  16. [16] N. Goléa et al., Fuzzy model reference adaptive control, IEEE Transactions on Fuzzy Systems, 10, 2002, 436–444.
  17. [17] V. Mayuresh & Kothare et al., Level control in the steam generator of a nuclear power plant, IEEE Transactions On Control Systems Technology, 8, 2000, 55–69.
  18. [18] M. Gyun Na et al., Design of a genetic fuzzy controller for the nuclear steam generator water level control, IEEE Transactions on Nuclear Science, 45, 1998, 2261–2271. 193
  19. [19] S.R. Munasinghe et al., Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants, IEEE Transactions on Nuclear Science, 2, 2005, 421–429.
  20. [20] X.J. Liu & F. Lara-Rosano, Model-reference adaptive control using associate memory network, Control and Intelligent Systems, 33(1), 2004.
  21. [21] M. Tokuda, T. Yamamoto, & Y. Monden, A neural-net based PID controllers for nonlinear multivariable systems Control and Intelligent Systems, 33 (1), 2005.
  22. [22] C.-H. Lee & Y.-C. Lin, An adaptive type-2 fuzzy neural controller for nonlinear uncertain systems Control and Intelligent Systems, 33(1), 2005.
  23. [23] M. Kosaka & H. Shibata, Auto-tuning of adaptive control with dead zone Control and Intelligent Systems, 34(1), 2006, 30–36.

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