Design of non-parametric process-specific optimal tuning rules for PID control of flow loops

Abstract The problem of designing optimal process-specific rules for non-parametric tuning is undertaken in the paper. It is shown that producing non-parametric process-specific optimal tuning rules for PID controllers leads to the problem that can be characterized as optimization under uncertainty. This happens due to the fact that tuning rules, unlike tuning constants, are produced not for a particular process or plant model but for a set of models from a certain domain. The novelty of the proposed approach is that the problem of obtaining optimal tuning rules for a flow process is formulated and solved as a problem of optimization of an integral performance criterion parametrized through values that define the domain of available process models. The considered non-parametric tuning assumes the use of the modified relay feedback test (MRFT) recently proposed in the literature. It allows one to tune the PID controller satisfying the requirements to gain or phase margins that is achieved through coordinated selection of tuning rules and test parameters. This approach constitutes a holistic approach to tuning. In the present paper, optimal tuning rules coupled with MRFT, for flow loops, are proposed. Final results are presented in the form of tables containing coefficients of optimal tuning rules for the PI controller, obtained for a number of specified gain margins. The produced non-parametric tuning rules well agree with the practice of loop tuning.

[1]  Urmila M. Diwekar Optimization Under Uncertainty , 2008 .

[2]  James E. Falk Technical Note - Exact Solutions of Inexact Linear Programs , 1976, Oper. Res..

[3]  Godwin C. Ovuworie,et al.  Mathematical Programming: Structures and Algorithms , 1979 .

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

[5]  I. Boiko Modified relay feedback test and its use for non-parametric loop tuning , 2008, 2008 American Control Conference.

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

[7]  Arkadi Nemirovski,et al.  Robust Convex Optimization , 1998, Math. Oper. Res..

[8]  William L. Luyben,et al.  Essentials of Process Control , 1996 .

[9]  Antonella Ferrara,et al.  Output tracking control of uncertain nonlinear second-order systems , 1997, Autom..

[10]  Laurent El Ghaoui,et al.  Robust Solutions to Least-Squares Problems with Uncertain Data , 1997, SIAM J. Matrix Anal. Appl..

[11]  D. P. Atherton,et al.  Nonlinear Control Engineering-Describing Function Analysis and Design , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Antonio Visioli,et al.  Practical PID Control , 2006 .

[13]  R. Rockafellar,et al.  Sensitivity analysis of solutions to generalized equations , 1994 .

[14]  R. Tyrrell Rockafellar,et al.  Scenarios and Policy Aggregation in Optimization Under Uncertainty , 1991, Math. Oper. Res..

[15]  I. Boiko,et al.  Modified relay feedback test: Industrial loop tuner implementation and experiments , 2009, 2009 American Control Conference.

[16]  Igor M. Boiko,et al.  Loop tuning with specification on gain and phase margins via modified second-order sliding mode control algorithm , 2012, Int. J. Syst. Sci..

[17]  A. Fiacco,et al.  Sensitivity analysis in nonlinear programming under second order assumptions , 1985 .

[18]  Igor Boiko,et al.  Analysis of dynamic nonlinearity of flow control loop through modified relay test probing , 2010, Int. J. Control.

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

[20]  Nikolaos V. Sahinidis,et al.  Optimization under uncertainty: state-of-the-art and opportunities , 2004, Comput. Chem. Eng..