Experimental comparison of new adaptive PI controllers based on the ultra-local model parameter identification

This paper is devoted to an experimental comparison between two different methods of ultra-local model control. The concept of the first proposed technique is based on the linear system resolution technique to estimate the ultra-local model parameters. The second proposed method is based on the linear adaptive observer which allows the joint estimation of state and unknown system parameters. The closed-loop control is implemented via an adaptive PID controller. In order to show the efficiency of these two control strategies, experimental validations are carried out on a two-tank system. The experimental results show the effectiveness and robustness of the proposed controllers.

[1]  Hajer Thabet,et al.  Towards an ultra-local model control of two-tank-system , 2016 .

[2]  Abdouramane Moussa Ali,et al.  Adaptive observer based fault diagnosis applied to differential-algebraic systems , 2013 .

[3]  Qinghua Zhang Adaptive Observer for MIMO Linear Time Varying Systems , 2001 .

[4]  Mounir Ayadi,et al.  Digital flatness-based robust controller applied to a thermal process , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[5]  Mounir Ayadi,et al.  Design of Fuzzy Flatness-Based Controller for a DC Drive , 2010, Control. Intell. Syst..

[6]  Y. D. Landau,et al.  Adaptive control: The model reference approach , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  M. Fliess,et al.  An algebraic framework for linear identification , 2003 .

[8]  Hajer Thabet,et al.  Ultra-local model control based on an adaptive observer , 2014, 2014 IEEE Conference on Control Applications (CCA).

[9]  Adi Ben-Israel,et al.  Generalized inverses: theory and applications , 1974 .

[10]  Aidan O'Dwyer,et al.  Handbook of PI and PID controller tuning rules , 2003 .

[11]  Cédric Join,et al.  Revisiting some practical issues in the implementation of model-free control , 2011 .

[12]  Qinghua Zhang,et al.  Adaptive observer for multiple-input-multiple-output (MIMO) linear time-varying systems , 2002, IEEE Trans. Autom. Control..

[13]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[14]  M. Fliess,et al.  Flatness and defect of non-linear systems: introductory theory and examples , 1995 .

[15]  Cédric Join,et al.  Model-free control and intelligent PID controllers: towards a possible trivialization of nonlinear control? , 2009, ArXiv.

[16]  Brian D. O. Anderson,et al.  Stability of adaptive systems: passivity and averaging analysis , 1986 .

[17]  William S. Levine,et al.  The Control Handbook , 2005 .

[18]  Cédric Join,et al.  Model-free control , 2013, Int. J. Control.

[19]  Qinghua Zhang,et al.  Adaptive observer with exponential forgetting factor for linear time varying systems , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[20]  Carlos García Rodríguez,et al.  Algebraic Identification and Estimation Methods in Feedback Control Systems , 2014 .

[21]  Hebertt Sira-Ramírez,et al.  Closed-loop parametric identification for continuous-time linear systems via new algebraic techniques , 2007 .