Nonlinear Adaptive PID Control for a Semi-Batch Reactor Based On an RBF Network
Control of a semi-batch polymerization reactor using
an adaptive radial basis function (RBF) neural network method is
investigated in this paper. A neural network inverse model is used to
estimate the valve position of the reactor; this method can identify the
controlled system with the RBF neural network identifier. The
weights of the adaptive PID controller are timely adjusted based on
the identification of the plant and self-learning capability of RBFNN.
A PID controller is used in the feedback control to regulate the actual
temperature by compensating the neural network inverse model
output. Simulation results show that the proposed control has strong
adaptability, robustness and satisfactory control performance and the
nonlinear system is achieved.
[1] R. W. Chylla and D. R. Haase, "Temperature control of semibatch
polymerization reactors.," Computer Chem. Engng, vol. 17, pp. 257-264,
1993.
[2] K. Graichen, V. Hagenmeyer, and M. Zeitz, "Adaptive Feedforward
Control with Parameter Estimation for the Chylla-Haase Polymerization
Reactor," in Decision and Control, 2005 and 2005 European Control
Conference. CDC-ECC '05. 44th IEEE Conference on, 2005, pp. 3049-
3054.
[3] K. Graichen, V. Hagenmeyer, and M. Zeitz, "Feedforward control with
online parameter estimation applied to the Chylla–Haase reactor
benchmark," Journal of Process Control, vol. 16, pp. 733-745, 2006.
[4] A. Helbig, O. Abel, and W. Marquardt, "Model predictive control for
online optimization of semi-batch reactors," in American Control
Conference, 1998. Proceedings of the 1998, 1998, pp. 1695-1699, vol.3.
[5] A. Helbig, O. Abel, A. M'hamdi, and W. Marquardt, "Analysis and
Nonlinear Model Predictive Control of the Chylla-Naase Benchmark
Problem," presented at the Proc.UKACC Int. Conf.control, 1996.
[6] A. Bhat and R. N. Banavar, "The Chylla-Haase Problem: A Neural
Network Controller," presented at the International Conference on
Control Applications, Trieste, Italy, 1998.
[7] T. Clarke-Pringle and J. F. MacGregor, "Nonlinear adaptive temperature
control of multi-product, semi-batch polymerization reactors,"
Computers & Chemical Engineering, vol. 21, pp. 1395-1409, // 1997.
[8] C. W. Ng and M. A. Hussain, "Hybrid neural network—prior knowledge
model in temperature control of a semi-batch polymerization process,"
Chemical Engineering and Processing: Process Intensification, vol. 43,
pp. 559-570, 2004.
[9] S. Lucia, T. Finkler, D. Basak, and S. Engell, "A new Robust NMPC
Scheme and its Application to a Semi-batch Reactor Example,"
presented at the 8th IFAC Symposium on Advanced Control of
Chemical Processes, The International Federation of Automatic Control,
Furama Riverfront, Singapore, 2012.
[10] T. F. Finkler, S. Lucia, M. B. Dogru, and S. Engell, "Simple Control
Scheme for Batch Time Minimization of Exothermic Semibatch
Polymerizations," Ind. Eng. Chem., vol. 17, pp. 5906–5920, 2013.
[11] M.-A. Beyer, W. Grote, and G. Reinig, "Adaptive exact linearization
control of batch polymerization reactors using a Sigma-Point Kalman
Filter," Journal of Process Control, vol. 18, pp. 663-675, 2008.
[12] M. Pottmann and D. E. Seborg, "A nonlinear Predictive Control Strategy
Based on Radial Basis Function Models," Computer Chem. Engng, vol.
21, pp. 965-980, 1997.
[13] D. L. Yu and Y. J. Zhai, "Radial-basis-function-based feedforward–
feedback control for air–fuel ratio of spark ignition engines,"
Proceedings of the Institution of Mechanical Engineers, Part D: Journal
of Automobile Engineering, vol. 222, pp. 415-428, 2008.
[14] D. L. Yu, J. B. Gomm, and D. Williams, "Online predictive control of a
chemical process using neural network models" presented at the
Proc.14th IFAC Congress, Beijing, 1999.
[15] Z. Ming-guang, L. Wen-hui, and L. Man-qiang, "Adaptive PID Control
Strategy Based on RBF Neural Network Identification," in Neural
Networks and Brain, 2005. ICNN&B '05. International Conference on,
2005, pp. 1854-1857.
[1] R. W. Chylla and D. R. Haase, "Temperature control of semibatch
polymerization reactors.," Computer Chem. Engng, vol. 17, pp. 257-264,
1993.
[2] K. Graichen, V. Hagenmeyer, and M. Zeitz, "Adaptive Feedforward
Control with Parameter Estimation for the Chylla-Haase Polymerization
Reactor," in Decision and Control, 2005 and 2005 European Control
Conference. CDC-ECC '05. 44th IEEE Conference on, 2005, pp. 3049-
3054.
[3] K. Graichen, V. Hagenmeyer, and M. Zeitz, "Feedforward control with
online parameter estimation applied to the Chylla–Haase reactor
benchmark," Journal of Process Control, vol. 16, pp. 733-745, 2006.
[4] A. Helbig, O. Abel, and W. Marquardt, "Model predictive control for
online optimization of semi-batch reactors," in American Control
Conference, 1998. Proceedings of the 1998, 1998, pp. 1695-1699, vol.3.
[5] A. Helbig, O. Abel, A. M'hamdi, and W. Marquardt, "Analysis and
Nonlinear Model Predictive Control of the Chylla-Naase Benchmark
Problem," presented at the Proc.UKACC Int. Conf.control, 1996.
[6] A. Bhat and R. N. Banavar, "The Chylla-Haase Problem: A Neural
Network Controller," presented at the International Conference on
Control Applications, Trieste, Italy, 1998.
[7] T. Clarke-Pringle and J. F. MacGregor, "Nonlinear adaptive temperature
control of multi-product, semi-batch polymerization reactors,"
Computers & Chemical Engineering, vol. 21, pp. 1395-1409, // 1997.
[8] C. W. Ng and M. A. Hussain, "Hybrid neural network—prior knowledge
model in temperature control of a semi-batch polymerization process,"
Chemical Engineering and Processing: Process Intensification, vol. 43,
pp. 559-570, 2004.
[9] S. Lucia, T. Finkler, D. Basak, and S. Engell, "A new Robust NMPC
Scheme and its Application to a Semi-batch Reactor Example,"
presented at the 8th IFAC Symposium on Advanced Control of
Chemical Processes, The International Federation of Automatic Control,
Furama Riverfront, Singapore, 2012.
[10] T. F. Finkler, S. Lucia, M. B. Dogru, and S. Engell, "Simple Control
Scheme for Batch Time Minimization of Exothermic Semibatch
Polymerizations," Ind. Eng. Chem., vol. 17, pp. 5906–5920, 2013.
[11] M.-A. Beyer, W. Grote, and G. Reinig, "Adaptive exact linearization
control of batch polymerization reactors using a Sigma-Point Kalman
Filter," Journal of Process Control, vol. 18, pp. 663-675, 2008.
[12] M. Pottmann and D. E. Seborg, "A nonlinear Predictive Control Strategy
Based on Radial Basis Function Models," Computer Chem. Engng, vol.
21, pp. 965-980, 1997.
[13] D. L. Yu and Y. J. Zhai, "Radial-basis-function-based feedforward–
feedback control for air–fuel ratio of spark ignition engines,"
Proceedings of the Institution of Mechanical Engineers, Part D: Journal
of Automobile Engineering, vol. 222, pp. 415-428, 2008.
[14] D. L. Yu, J. B. Gomm, and D. Williams, "Online predictive control of a
chemical process using neural network models" presented at the
Proc.14th IFAC Congress, Beijing, 1999.
[15] Z. Ming-guang, L. Wen-hui, and L. Man-qiang, "Adaptive PID Control
Strategy Based on RBF Neural Network Identification," in Neural
Networks and Brain, 2005. ICNN&B '05. International Conference on,
2005, pp. 1854-1857.
@article{"International Journal of Information, Control and Computer Sciences:67996", author = "Magdi M. Nabi and Ding-Li Yu", title = "Nonlinear Adaptive PID Control for a Semi-Batch Reactor Based On an RBF Network", abstract = "Control of a semi-batch polymerization reactor using
an adaptive radial basis function (RBF) neural network method is
investigated in this paper. A neural network inverse model is used to
estimate the valve position of the reactor; this method can identify the
controlled system with the RBF neural network identifier. The
weights of the adaptive PID controller are timely adjusted based on
the identification of the plant and self-learning capability of RBFNN.
A PID controller is used in the feedback control to regulate the actual
temperature by compensating the neural network inverse model
output. Simulation results show that the proposed control has strong
adaptability, robustness and satisfactory control performance and the
nonlinear system is achieved.
", keywords = "Chylla-Haase polymerization reactor, RBF neural
networks, feed-forward and feedback control.", volume = "8", number = "7", pages = "1260-6", }
