Linearizing controller for higher-degree nonlinear processes with compensation for modeling inaccuracies practical validation and future developments

This work shows the results of the practical implementation of the linearizing controller for the example laboratory pneumatic process of the third relative degree. Controller design is based on the Lie algebra framework but in contrast to the previous attempts, the on-line model update method is suggested to ensure offset-free control. The paper details the proposed concept and reports the experiences from the practical implementation of the suggested controller. The superiority of the proposed approach over the conventional PI controller is demonstrated by experimental results. Based on the experiences and the validation results, the possibilities of the potential application of the data-driven soft sensors for further improvement of the control performance are discussed.

[1]  R. Russell Rhinehart,et al.  Two simple methods for on-line incremental model parameterization , 1991 .

[2]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[3]  Bogdan Gabrys,et al.  Adaptive on-line prediction soft sensing without historical data , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[4]  Jacek Czeczot,et al.  Balance-based adaptive control methodology and its application to the non-isothermal CSTR , 2006 .

[5]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[6]  Bogdan Gabrys,et al.  Architecture for development of adaptive on-line prediction models , 2009, Memetic Comput..

[7]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[8]  Jacek Czeczot,et al.  Model-Based Adaptive Control of Fed-Batch Fermentation Process with the Substrate Consumption Rate Application , 1998 .

[9]  Gintaras V. Reklaitis Computers and chemical engineering: Best paper of 2004 , 2007, Comput. Chem. Eng..

[10]  A. Isidori Nonlinear Control Systems: An Introduction , 1986 .

[11]  Jacek Czeczot Balance-based adaptive control of a neutralisation process , 2006 .

[12]  Graham C. Goodwin,et al.  Virtual sensors for control applications , 2002, Annu. Rev. Control..

[13]  Costas Kravaris,et al.  Advances and selected recent developments in state and parameter estimation , 2013, Comput. Chem. Eng..

[14]  Q Liu,et al.  IMC-PID design based on model matching approach and closed-loop shaping. , 2014, ISA transactions.

[15]  Bogdan Gabrys,et al.  Adaptive Local Learning Soft Sensor for Inferential Control Support , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.

[16]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[17]  Bogdan Gabrys,et al.  Review of adaptation mechanisms for data-driven soft sensors , 2011, Comput. Chem. Eng..

[18]  Jacek Czeczot,et al.  General tuning procedure for the nonlinear balance-based adaptive controller , 2014, Int. J. Control.

[19]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[20]  G. R. Sullivan,et al.  Generic model control (GMC) , 1988 .

[21]  Witold Klopot,et al.  Flexible function block for PLC-based implementation of the Balance-Based Adaptive Controller , 2012, 2012 American Control Conference (ACC).

[22]  Dale E. Seborg,et al.  Nonlinear Process Control , 1996 .

[23]  Bogdan Gabrys,et al.  Local learning‐based adaptive soft sensor for catalyst activation prediction , 2011 .