Controlling Nonlinear Processes, using Laguerre Functions Based Adaptive Model Predictive Control (AMPC) Algorithm

Authors

Department of Chemical Engineering, Ferdowsi University of Mashhad

Abstract

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control continuous stirred tank reactor (CSTR) process temperature runaways. Simulation result reveals that AMPC has a good performance in set-point tracking and load rejection. For comparison, a nonlinear model predictive control based on Laguerre- wiener model was also applied to the process. Simulation result demonstrates that the two controllers have the same performance in set point tracking and load rejection problem.

Keywords

References

[1] Micheal, A.H. (2001). “Nonlinear model predictive control: current status and future direction.” Computer and Chemical Engineering, PP. 187-202.
[2] Zervos, C.C. and Dumont, G.Y. (1988). “Deterministic adaptive control based on Laguerre series representation.” International Journal of Control, Vol. 48, PP. 2333-2359.
[3] Zhang, H.T., Xu, Z.F. and Li, S.F. (2002). “An adaptive predictive control based on Laguerre function model for diffusion furnace.” Journal of System Engineering and Electronics, Vol. 24, PP. 54-57.
[4] Zhang, H.T., Chen, Z.H. and Li, S.F. (2004). “Multi-variable system’s Laguerre predictive control algorithm and the improvement for this algorithm.” Control Engineering of China, Vol. 11, PP. 55–58.
[5] Zhang, H.T., Chen, Z.H., Qin, T., Wang, L. and Xiang, W. (2004). “An improved multivariable system’s adaptive predictive control algorithm based on Laguerre Function.” Proceedings of IEEE World Congress on Intelligent Control and Automation, Hangzhou, People’s Republic of China, PP. 713–718.
[6] Fruzzetti, K., Palazoglu, A. and McDonald, K. (1997). “Nonlinear model predictive control using Hammerstein models.” Journal of Process Control, Vol. 7, PP. 31–41.
[7] Juan, C.G. and Enrique, B. (2004). “Identification of block oriented nonlinear systems using orthonormal bases.” Journal of Process Control, Vol. 14, PP. 685–697.
[8] Zhu, G.Y., Henson, M.A. and Ogunnaike, B.A. (2000). “A hybrid model predictive control strategy for nonlinear plant-wide control.” Journal of Process Control, Vol. 10, PP. 449–458.
[9] Wahlberg, B. and Ma kila, P.M. (1996). “On approximation of stable linear dynamical systems using Laguerre and Kautz Functions.” Automatica, Vol. 32, PP. 693–708.
[10] Zheng, Q.B., Shu, D.Q. and Wang, S.H. (1989). “Intelligent self-tuning model predictive control.” Proceedings of International Conference on Signals and Systems, Dalian, China.
[11] Khaksar, M. and Shahrokhi, M. (2008). “Comparison of nonlinear model predictive control with generalize model predictive control.” 12th Congress of Chemical Engineering, Tabriz, Iran.
[12] Ljung, L. (1999). System Identification: Theory for the user, Prentice Hall, Englewood Cliffs, N.J.
[13] Luyben, W. L. (2002). Plant wide dynamic simulators in chemical processing and control, Marcel Dekker.
[14] Nikravesh, M. (1994). Dynamic neural network control, Ph.D. Dissertation, University of South Carolina, Columbia, S.C.

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