PI-controller parameters tuning method to reject disturbances acting on heating furnaces

On-line Kp, Ki gains adjustment of a Pi-controller to reject disturbances acting on a heating furnace is the main scope of this research. A neural tuner is used to resolve considered problem. it consists of two neural networks in order to ensure quality of control for heating and cooling processes. An additional network is integrated in its structure to enable disturbances attenuation. Network outputs are Kp, Ki values. Time moments when such networks need to be trained and learning rate values are determined by a rule base. A set of rules developed to reject step-like and impulse disturbances acting on the plant output is shown, as well as the tuner structure. The SNOL 40/1200 muffle electroheating furnace is used as a plant for experiments. Obtained results show the total amount of time spent on disturbance rejection may be reduced by 20% using the neural tuner in comparison with a control system with Pi-controller with fixed gains.

[1]  Tore Hägglund,et al.  Advanced PID Control , 2005 .

[2]  K. L. Anderson,et al.  A rule-based adaptive PID controller , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[3]  Masayoshi Tomizuka,et al.  Fuzzy gain scheduling of PID controllers , 1993, IEEE Trans. Syst. Man Cybern..

[4]  Hasan Erdal,et al.  Optimization of PID Controllers Using Ant Colony and Genetic Algorithms , 2013, Studies in Computational Intelligence.

[5]  A. Glushchenko,et al.  Rules for parameter adjustment in a PI controller for metallurgical heaters , 2015, Steel in Translation.

[6]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[7]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[8]  Engin Yesil,et al.  An Intelligent Hybrid Fuzzy Pid Controller , 2006 .

[9]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[10]  A. Alexandrov.  Frequencial adaptive PID-controller , 1999, 1999 European Control Conference (ECC).

[11]  Bernd-Markus Pfeiffer.  Towards ‘plug and control’: self‐tuning temperature controller for PLC , 2000 .

[12]  Tore Hägglund,et al.  Automatic Tuning and Adaptation for PID Controllers - A Survey , 1992 .

[13]  K. L. Chien,et al.  On the Automatic Control of Generalized Passive Systems , 1952, Journal of Fluids Engineering.

[14]  Ming-Chung Fang,et al.  The application of the self-tuning neural network PID controller on the ship roll reduction in random waves , 2010 .

[15]  Husain Ahmed,et al.  Performance Assessment of Tuning Methods for PID Controller Parameter used for Position Control of DC Motor , 2014 .

[16]  David Clarke,et al.  On the automatic tuning and adaptation of PID controllers , 2006 .

[17]  Tore Hägglund,et al.  Automatic Tuning and Adaptation for PID Controllers—A Survey , 1992 .

[18]  K. Mimura,et al.  Experimental study of PID auto-tuning for unsymmetrical processes , 2009, 2009 ICCAS-SICE.

[19]  Carlos-M. Astorga-Zaragoza,et al.  Bounded neuro-control position regulation for a geared DC motor , 2010, Eng. Appl. Artif. Intell..

[20]  U. Kuhn.  Eine praxisnahe Einstellregel für PID-Regler: die T-Summen-Regel , 1995 .

[21]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[22]  Junghui Chen,et al.  Applying neural networks to on-line updated PID controllers for nonlinear process control , 2004 .

[23]  Y. I. Eremenko,et al.  On applying neural tuner to PI-controller parameters calculation for heating furnaces control , 2015, 2015 International Siberian Conference on Control and Communications (SIBCON).

[24]  Marzuki Khalid,et al.  Neuro-control and its applications , 1996 .