Development of control quality factor for HVAC control loop performance assessment I—Methodology (ASHRAE RP-1587)

This article is the second paper from the ASHRAE research project RP-1587, focusing on the methodology of obtaining a control quality factor (CQF). This article presents a development of two CQFs for assessing the heating, ventilation, and air-conditioning (HVAC) control loop performance. Both CQFs are able to detect whether the control loop is able to maintain the setpoint and identify the loop’s ability to handle disturbances. In addition, the reversal behaviors are assessed as well. The first CQF (CQF-Harris) is proposed based on the normalized Harris index using the recursive least squares method. This recursive least squares method is selected because of its computational efficiency compared with the maximum likelihood estimation method. The second CQF (CQF-EWMA) is based on the exponentially weighted moving average of the error ratio. The assessment scale of excellent, good, fair, bad, and failed, which indicates the quality of the HVAC control loops, is established as well. The sensitivity analysis for both CQFs is also conducted, and it provides insights on choosing the appropriate parameters to compute such CQFs. Such parameters include the sampling frequency, the length of the moving window, and the variance of the unmeasured disturbance. The field evaluations and tests of the proposed CQFs for simulated control loops and real control loops can be found in the companion paper with the title “Development of Control Quality Factor for HVAC Control Loop Performance Assessment—III: Field Testing and Results (ASHRAE RP-1587).”

[1]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[2]  Timothy I. Salsbury,et al.  Two new normalized EWMA-based indices for control loop performance assessment , 2015, 2015 American Control Conference (ACC).

[3]  T. Edgar,et al.  Performance Assessment of Run-to-Run EWMA Controllers , 2007 .

[4]  Yuri A.W. Shardt,et al.  Determining the state of a process control system: Current trends and future challenges , 2012 .

[5]  Antonius Yudi Sendjaja,et al.  Achievable PID performance using sums of squares programming , 2009 .

[6]  J. Stuart Hunter,et al.  The exponentially weighted moving average , 1986 .

[7]  John E. Seem,et al.  On-line monitoring and fault detection , 1999 .

[8]  T. V. Ramanathan,et al.  ORDER SELECTION IN ARMA MODELS USING THE FOCUSED INFORMATION CRITERION , 2011 .

[9]  Yanfei Li,et al.  EVALUATING CONTROL PERFORMANCE ON BUILDING HVAC CONTROLLERS , 2016 .

[10]  Marcus B. Perry,et al.  The Exponentially Weighted Moving Average , 2010 .

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

[12]  Xiaohui Zhou,et al.  Development of control quality factor for HVAC control loop performance assessment—II: Field testing and results (ASHRAE RP-1587) , 2019 .

[13]  John E. Seem,et al.  Integrated Control and Fault Detection of Air-Handling Units , 2009 .

[14]  Thomas F. Edgar,et al.  PID control performance assessment: The single‐loop case , 2004 .

[15]  James Fan,et al.  PI auto-tuning and performance assessment in HVAC systems , 2013, 2013 American Control Conference.

[16]  Hu Yuanbiao Iterative and recursive least squares estimation algorithms for moving average systems , 2013 .

[17]  Lijun Xu,et al.  A New Method for Variance Estimation of White Noise Corrupting a Signal , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[18]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[19]  Sirish L. Shah,et al.  Practical Issues in Multivariable Feedback Control Performance Assessment , 1997 .

[20]  M. M. Ardehali,et al.  The National Building Controls Information Program , 2002 .

[21]  N. Hjort,et al.  The Focused Information Criterion , 2003 .

[22]  P. Rousseeuw,et al.  Alternatives to the Median Absolute Deviation , 1993 .

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

[24]  Timothy I. Salsbury Continuous-time model identification for closed loop control performance assessment , 2007 .

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

[26]  Biao. Huang,et al.  Multivariate statistical methods for control loop performance assessment , 1997 .

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

[28]  Yanfei Li,et al.  HVAC control loop performance assessment: A critical review (1587-RP) , 2017 .

[29]  Yuanbiao Hu,et al.  Iterative and recursive least squares estimation algorithms for moving average systems , 2013, Simul. Model. Pract. Theory.

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

[31]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

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

[33]  James M. Lucas,et al.  Exponentially weighted moving average control schemes: Properties and enhancements , 1990 .

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

[35]  Yanfei Li,et al.  Assessment of different data-driven algorithms for ahu energy consumption predictions , 2015 .

[36]  Yang Zhang,et al.  Improved methods in statistical and first principles modeling for batch process control and monitoring , 2008 .

[37]  T. J. Harris,et al.  Performance assessment of multivariable feedback controllers , 1996, Autom..

[38]  Sirish L. Shah,et al.  Recursive least squares based estimation schemes for self‐tuning control , 1991 .

[39]  Doreen Meier,et al.  Introduction To Stochastic Control Theory , 2016 .

[40]  John E. Seem,et al.  ON-LINE MONITORING AND FAULT DETECTION OF CONTROL SYSTEM PERFORMANCE , 1997 .

[41]  Zhi-huan Song,et al.  PID control performance assessment using iterative convex programming , 2012 .

[42]  S. Qin,et al.  Projection based MIMO control performance monitoring: I—covariance monitoring in state space , 2003 .

[43]  S. Lakshminarayanan,et al.  A Filter-Based Approach for Performance Assessment and Enhancement of SISO Control Systems† , 2005 .