Design and Analysis of Robust Multivariable PI Controller for Paper Machine Headbox Using Particle Swarm Optimisation
Most of the control loops in paper machine are multivariable loops. Due to multivariable nature of the loops, there is probability that loop have significant interaction. So, the prime objective of designing controllers for multivariable systems is that the process variations are minimized to get desired response. This paper presents a multi-variable control system approach to design PI controller using Particle Swarm Optimisation (PSO) for paper machine headbox. Paper machine headbox is a 2-input and 2-output sub-process of paper machine. The major parameters to be controlled in headbox are Total Head (Pressure) and Stock Level. The performance of PSO based multivariable PI controller has been compared with three conventional controller tuning techniques. The performance assessment of multivariable controllers has been done on the basis of time response, frequency response, performance indices and robustness.
Chien, I. L.(1990). Consider IMC tuning to improve controller performance. Chemistry Engineering Progress, 86(10), 33-41.
Clerc, M. (1999). The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)(Vol. 3, pp. 1951-1957). IEEE.
Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence (Morgan Kaufmann series in evolutionary computation). Morgan Kaufmann Publishers.
Eberhart, R., Simpson, P., & Dobbins, R. (1996). Computational intelligence PC tools. Boston, MA: Academic, Press Professional.
El-Shorbagy, M. A., & Hassanien, A. E. (2018). Particle swarm optimization from theory to applications. International Journal of Rough Sets and Data Analysis, 5(2), 1-24.
Garcia, C. E., &Morari, M.(1982). Internal model control: A unifying review and some new results. Industrial and Engineering Chemistry Process Design and Development, 21(2), 308-323.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, IEEE Press, 1942–1948.
Nissinen, A., Koivo, H. N., & Huhtelin, T. (1996). Multivariable PI control of industrial paper machine headboxes. IFAC Proceedings Volumes, 29(1), 6686-6691.
Paattilammi, J., & Makila, P. M. (2000). Fragility and robustness: A case study on paper machine headbox control. IEEE Control Systems Magazine, 20(1), 13-22.
Rivera, D. E., Morari, M., & Skogestad, S. (1986). Internal modelcontrol: PID controller design. Industrial and Engineering Chemistry Process Design and Development, 25(1), 252-265.
Saini, P., & Kumar, R. (2018). Brief review and mathematical modelling of air cushioned pressurized paper machine headbox. International Journal of Engineering and Technology (UAE), 7(3.4), 57-65.
Saini, P., & Kumar, R. (2018). Stability analysis of paper machine headbox using a new PI(D) tuning technique. International Journal of Engineering and Technology, 7(2.6), 39-45.
Saini, P., & Kumar, R. (2019). Design of IMC based PI controller for paper machine headbox. Journal of Graphic Era University, 7(1), 71-82.
Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309.
Tyreus, B. D., & Luyben, W. L. (1992). Tuning PI controllers for integrator/dead time processes. Industrial & Engineering Chemistry Research, 31(11), 2625–2628.
Xiao, Z., & Wang, M. (2009). Study on the NN decoupling control system of air-cushioned headbox. Computer and Information Science, 2(3), 87-93.
Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of the ASME, 64, 759-768.