A neural network approach to control performance assessment

Purpose – The purpose of this paper is to present a neural network approach to control performance assessment.Design/methodology/approach – The performance index under study is based on the minimum variance control benchmark, a radial basis function network (RBFN) is used as the pre‐whitening filter to estimate the white noise sequence, and a stable filtering and correlation analysis method is adopted to calculate the performance index by estimating innovations sequence using the RBFN pre‐whitening filter. The new approach is compared with the auto‐regressive moving average model and the Laguerre model methods, for both linear and nonlinear cases.Findings – Simulation results show that the RBFN approach works satisfactorily for both linear and nonlinear examples. In particular, the proposed scheme shows merits in assessing controller performance for nonlinear systems and surpasses the Laguerre model method in parameter selection.Originality/value – A RBFN approach is proposed for control performance asses...

[1]  K. Åström.  Introduction to Stochastic Control Theory , 1970 .

[2]  Sheng Chen,et al.  Practical identification of NARMAX models using radial basis functions , 1990 .

[3]  T. Harris,et al.  Performance assessment measures for univariate feedback control , 1992 .

[4]  Mohieddine Jelali,et al.  An overview of control performance assessment technology and industrial applications , 2006 .

[5]  C. T. Seppala,et al.  A review of performance monitoring and assessment techniques for univariate and multivariate control systems , 1999 .

[6]  S. Joe Qin,et al.  Control performance monitoring — a review and assessment , 1998 .

[7]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[8]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[9]  Manfred Morari,et al.  Performance monitoring of control systems using likelihood methods , 1996, Autom..

[10]  Thomas J. Harris,et al.  Controller assessment for a class of non-linear systems , 2007 .

[11]  Luigi Piroddi,et al.  A Neural Network Approach to Generalized Minimum Variance Control of Nonlinear Systems , 1993 .

[12]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[13]  P.-G. Eriksson,et al.  Some aspects of control loop performance monitoring , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[14]  G.A. Dumont,et al.  Control loop performance monitoring , 1996, IEEE Trans. Control. Syst. Technol..

[15]  Thomas J. Harris,et al.  Performance assessment measures for univariate feedforward/feedback control , 1993 .

[16]  Karl Johan Åström,et al.  Assessment of Achievable Performance of Simple Feedback Loops , 1988 .

[17]  Wei Yu,et al.  Control performance assessment for nonlinear systems , 2010 .

[18]  H. Kushner.  Introduction to stochastic control , 1971 .

[19]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[20]  Chen Wei,et al.  Delay-dependent robust control design for uncertain multi-time-delayed linear systems , 2008, Int. J. Intell. Comput. Cybern..

[21]  Sirish L. Shah,et al.  Good, bad or optimal? Performance assessment of multivariable processes , 1997, Autom..

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

[23]  S. K. Park,et al.  Random number generators: good ones are hard to find , 1988, CACM.

[24]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[25]  J. D. Powell,et al.  Radial basis function approximations to polynomials , 1989 .

[26]  George W. Irwin,et al.  A fast nonlinear model identification method , 2005, IEEE Transactions on Automatic Control.

[27]  Biao Huang,et al.  Performance Assessment of Control Loops , 1999 .

[28]  Warren R. DeVries,et al.  Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate time-series analysis , 1978 .

[29]  Nina F. Thornhill,et al.  Refinery-wide control loop performance assessment , 1999 .

[30]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[31]  Alan J. Hugo,et al.  Performance assessment of single-loop industrial controllers , 2006 .

[32]  T. Harris.  Assessment of Control Loop Performance , 1989 .

[33]  Michael J. Grimble,et al.  Controller performance benchmarking and tuning using generalised minimum variance control , 2002, Autom..