A bio-knowledge based method to prevent control system instability

This study presents a novel bio-inspired method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative (PID) controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is firstly identified. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a liquid-level laboratory plant.

[1]  Tore Hägglund,et al.  The future of PID control , 2000 .

[2]  Emilio Corchado,et al.  A weighted voting summarization of SOM ensembles , 2010, Data Mining and Knowledge Discovery.

[3]  Gilles Duc,et al.  An interpolation method for gain-scheduling , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[4]  Yu-Sheng Lu Internal Model Control of Lightly Damped Systems Subject to Periodic Exogenous Signals , 2010, IEEE Transactions on Control Systems Technology.

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

[6]  Monica N. Nicolescu,et al.  A TRAINING SIMULATION SYSTEM WITH REALISTIC AUTONOMOUS SHIP CONTROL , 2007, Comput. Intell..

[7]  Lennart Ljung,et al.  System identification (2nd ed.): theory for the user , 1999 .

[8]  M. Safonov,et al.  Exact calculation of the multiloop stability margin , 1988 .

[9]  Mohammad Bagher Menhaj,et al.  Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks , 2010, Inf. Sci..

[10]  J. Hung,et al.  Evaluation of DSP-Based PID and Fuzzy Controllers for DC–DC Converters , 2009, IEEE Transactions on Industrial Electronics.

[11]  Massimo Canale,et al.  Robust Tuning of Low-Order Controllers via Uncertainty Model Identification , 1999, Eur. J. Control.

[12]  Bing Chen,et al.  Direct adaptive fuzzy control for nonlinear systems with time-varying delays , 2010, Inf. Sci..

[13]  Alfredo Milani,et al.  PLANNING IN REACTIVE ENVIRONMENTS , 2007, Comput. Intell..

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

[15]  Victor R. Lesser,et al.  A Constructive Graphical Model Approach for Knowledge‐Based Systems: A Vehicle Monitoring Case Study , 2003, Comput. Intell..

[16]  K. Åström,et al.  Revisiting The Ziegler‐Nichols Tuning Rules For Pi Control , 2002 .

[17]  Leandro dos Santos Coelho,et al.  Computational intelligence approach to PID controller design using the universal model , 2010, Inf. Sci..

[18]  Hao Ying,et al.  Fuzzy Gain-Scheduling Proportional–Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle , 2009, IEEE Transactions on Vehicular Technology.

[19]  David A. Mindell,et al.  Between Human and Machine: Feedback, Control, and Computing before Cybernetics , 2002 .

[20]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[21]  Mohammad Hassan Moradi,et al.  New techniques for PID controller design , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[22]  N. Munro,et al.  PID controllers: recent tuning methods and design to specification , 2002 .

[23]  Christos Mademlis,et al.  Gain-Scheduling Regulator for High-Performance Position Control of Switched Reluctance Motor Drives , 2010, IEEE Transactions on Industrial Electronics.

[24]  Daniel Graupe,et al.  Principles of Artificial Neural Networks , 2018, Advanced Series in Circuits and Systems.

[25]  Andrei Doncescu,et al.  Analytical and knowledge based approaches for a bioprocess supervision , 2010, Knowl. Based Syst..

[26]  Newton G. Bretas,et al.  Automatic tuning method for the design of supplementary damping controllers for flexible alternating current transmission system devices , 2009 .

[27]  Wilson J. Rugh,et al.  Analytical Framework for Gain Scheduling , 1990, 1990 American Control Conference.

[28]  Tore Hägglund,et al.  Automatic tuning of simple regulators with specifications on phase and amplitude margins , 1984, Autom..

[29]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[30]  Shankar P. Bhattacharyya,et al.  PID Controllers for Time Delay Systems , 2004 .

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

[32]  Álvaro Herrero,et al.  Neural projection techniques for the visual inspection of network traffic , 2009, Neurocomputing.

[33]  Bin Jiang,et al.  Knowledge mining technique based fault diagnosis of shape control system in a rolling process , 2010, 2010 Chinese Control and Decision Conference.

[34]  Naixue Xiong,et al.  A novel self-tuning feedback controller for active queue management supporting TCP flows , 2010, Inf. Sci..

[35]  Kazuhiko Hiramoto,et al.  Active Gain Scheduling: A Collaborative Control Strategy between LPV Plants and Gain Scheduling Controllers , 2007, 2007 IEEE International Conference on Control Applications.

[36]  Y. Rao,et al.  An integrated knowledge based system for sheet metal cutting-punching combination processing , 2009, Knowl. Based Syst..

[37]  Yun Li,et al.  PIDeasy and automated generation of optimal PID controllers , 1998 .