Neuro-fuzzy-based Smith predictor for FOPLDT process control

In this paper, a novel approach for a first-order-plus-long-dead-time (FOPLDT) process control has been developed. The Smith predictor (SP) is a well-known compensation scheme to deal with the problem of long dead time in process control. However, in the presence of process modelling errors in gain, time constant or time delay, the Smith predictor appears to have poor performance, as even instability may be caused as soon as model mismatch becomes significant. An application of neuro-fuzzy-based Smith predictor method is introduced to improve the robustness to modelling errors for FOPLDT process control. From simulation evaluation, this new proposed approach provides superior performance to the original Smith predictor and robustness to modelling errors.

[1]  M. Matausek,et al.  A modified Smith predictor for controlling a process with an integrator and long dead-time , 1996, IEEE Trans. Autom. Control..

[2]  Zheng Chen,et al.  Design of an Adaptive Self-Tuning Smith Predictor for a Time Varying Water Treatment Process , 2009 .

[3]  Zoubir Zouaoui,et al.  A neuro-fuzzy compensator based Smith predictive control for FOPLDT process , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[4]  C. C. Hang,et al.  A new Smith predictor for controlling a process with an integrator and long dead-time , 1994, IEEE Trans. Autom. Control..

[5]  Pingkang Li,et al.  Fuzzy Logic Control Optimal Realization Using GA for Multi-Area AGC Systems , 2006 .

[6]  Hao-guang Chen,et al.  Application of fuzzy logic to reduce modelling errors in PIDSP for FOPDT process control , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[7]  Julio E. Normey-Rico,et al.  Improving the robustness of dead-time compensating PI controllers , 1997 .

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  S. Kermiche,et al.  Fuzzy PID Controller Parameters Optimized by Genetic Algorithms , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[10]  Aleksandar Micic,et al.  On the modified Smith predictor for controlling a process with an integrator and long dead-time , 1999, IEEE Trans. Autom. Control..

[11]  Ching-Hung Lee,et al.  Identification and Fuzzy Controller Design for Nonlinear Uncertain Systems with Input Time-Delay , 2009 .

[12]  Pei-Guang Wang,et al.  Smith predictive control based on NN , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[13]  Eduardo F. Camacho,et al.  A unified approach to design dead-time compensators for stable and integrative processes with dead-time , 2002, IEEE Trans. Autom. Control..

[14]  Somanath Majhi,et al.  A new Smith predictor and controller for unstable and integrating processes with time delay , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[15]  Tien Chi Chen,et al.  Model reference robust speed control for induction-motor drive with time delay based on neural network , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[16]  O. J. M. Smith,et al.  A controller to overcome dead time , 1959 .

[17]  M. Elarafi,et al.  Modeling and control of pH neutralization using neural network predictive controller , 2008, 2008 International Conference on Control, Automation and Systems.

[18]  Eduardo F. Camacho,et al.  Unified approach for robust dead-time compensator design , 2009 .

[19]  Antonio Visioli,et al.  Fuzzy logic based set-point weight tuning of PID controllers , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Eduardo F. Camacho,et al.  Robust tuning of dead-time compensators for processes with an integrator and long dead-time , 1999, IEEE Trans. Autom. Control..

[21]  Ibrahim Kaya.  A new Smith predictor and controller for control of processes with long dead time. , 2003, ISA transactions.

[22]  Danying Gu,et al.  Optimal dead-time compensator design for stable and integrating processes with time delay , 2008 .

[23]  L. Wu,et al.  Self-learning fuzzy neural networks for control of uncertain systems with time delays , 1997, IEEE Trans. Syst. Man Cybern. Part B.