A Study on Minimum Variance Benchmark under Actuator Constrains and Stochastic Disturbances

Abstract:

As a benchmark to assess the performance of the control loops, MV (minimum variance) has been widely used in industrial process.MV control gives the lowest achievable output variance when there is no constraint on the actuator. In this paper, it shows that MV controller is no longer optimal and the mean of the output value is non-zero when the constraint is active and the parameter of the disturbance is approaching to one. In order to achieve the lowest variance of the output value, and the mean of the output value can be approaching to zero, a new approach is proposed in this paper. The proposed method is imposing a compensation value on the constrained MV control law, meanwhile, a new performance index function is introduced. As a result, it is believed that this method is more effective to the real industrial process for systems with actuator constraints and stochastic disturbances. Simulation example demonstrates the usefulness of this method.

• Periodical: Applied Mechanics and Materials (Volumes 719-720)
• Pages: 431-435
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.719-720.431
• Online since: January 2015
• Authors: Qing Yue Zhang*, Yan Ru Xu
• Keywords: Actuator Constraints, Compensation Value, Minimum Variance Benchmark, Performance Assessment

References

[1] Sendjaja A Y, Kariwala V. Minimum Variance Benchmark for Performance Assessment of Decentralized Controllers[J]. Industrial&Engineering Chemistry Research, 2012, 51(11): 4288-4298.

DOI: 10.1021/ie201361d

[2] Jelali M. An overview of control performance assessment technology and industrial applications[J]. Control Engineering Practice, 2006, 14(5): 441-466.

DOI: 10.1016/j.conengprac.2005.11.005

[3] Ko B S, Edgar T F. Performance assessment of constrained model predictive control systems[J]. AIChE Journal, 2001, 47(6): 1363-1371.

DOI: 10.1002/aic.690470613

[4] Åström K.J. Introduction to stochastic control. New York: Academic Press. (1970).

[5] Box G E P& Jenkins G M Time series analysis: forecasting and control[M]. San Francisco, CA: Holden-Day. (1970).

[6] Harris T J. Assessment of control loop performance[J]. The Canadian Journal of Chemical Engineering, 1989, 67(5): 856-861.

[7] Xia H, Majecki P, Ordys A, et al. Calculating setpoint range under actuator saturation and stochastic disturbance[C]. IEEE, 2005: 1069-1072.

DOI: 10.1109/cca.2005.1507272

[8] Bezanson L W. Scalar quadratic control with amplitude constraints[J]. Electronics Letters, 1984, 20(6): 246-247.

DOI: 10.1049/el:19840165

[9] Saha P, Mukta C B, Choudhury M A A S. Performance Assessment of Control Loops[J]. International Journal, (2012).

[10] GoodwinG C. Amplitude constrained minimum variance controller[J]. Electronics Letters, 1972, 8(7): 181-182.

[11] Patwardhan R S, Shah S L. Issues in performance diagnostics of model-based controllers[J]. Journal of Process Control, 2002, 12(3): 413-427.

DOI: 10.1016/s0959-1524(01)00043-9

[12] Huang B, Shah S L. Performance assessment of control loops: theory and applications[M]. Springer, (1999).