[1]
|
Cui H, Jacobsen E W. Performance limitations in decentralized control. Journal of Process Control, 2002, 12(4): 485- 494[2] Scattolini R. Architectures for distributed and hierarchical model predictive control —— a review. Journal of Process Control, 2009, 19(5): 723-731[3] Stewart B T, Venkat A N, Rawlings J B, Wright S J, Pannocchia G. Cooperative distributed model predictive control. Systems Control Letters, 2010, 59(8): 460-469[4] Harris T J. Assessment of control loop performance. The Canadian Journal of Chemical Engineering, 1989, 67(5): 856-861[5] Huang B, Kadali R. Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach. Vol. 374. New York: Springer Verlag, 2008[6] Gao J P, Patwardhan R, Akamatsu K, Hashimoto Y, Emoto G, Shah S L, Huang B. Performance evaluation of two industrial MPC controllers. Control Engineering Practice, 2003, 11(12): 1371-1387[7] Yang M Y, Jin X M, Chen F. Performance assessment of model predictive control in industrial distillation column. In: Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, China: IEEE, 2004. 637-641[8] Huang B, Shah S L. Performance Assessment of Control Loops: Theory and Applications. New York: Springer Verlag, 1999[9] Shah S L, Patwardhan R, Huang B. Multivariate controller performance analysis: methods, applications and challenges. AIChE Symposium Series, New York, USA, 2002. 190-207[10] Shah S L, Mitchell W, Shook D. Challenges in the detection, diagnosis and visualisation of controller performance data. Computing and Control Engineering Journal, 2005, 16(4): 30-34[11] Patwardhan R S, Shah S L, Qi K Z. Assessing the performance of model predictive controllers. The Canadian Journal of Chemical Engineering, 2002, 80(5): 954-966[12] Jain M, Lakshminarayanan S. Estimating performance enhancement with alternate control strategies for multiloop control systems. Chemical Engineering Science, 2007, 62(17): 4644-4658[13] Kariwala V, Forbes J F, Meadows E S. Minimum variance benchmark for decentralized controllers. In: Proceedings of the 2005 American Control Conference. Portland, OR, USA: IEEE, 2005. 1437-1442[14] Kariwala V. Fundamental limitation on achievable decentralized performance. Automatica, 2007, 43(10): 1849- 1854[15] De Oliveira M C, Geromel J C, Bernussou J. Extended H2 and H∞ norm characterizations and controller parametrizations for discrete-time systems. International Journal of Control, 2002, 75(9): 666-679[16] Geromel J C, Peres P L D. Decentralized load-frequency control. IEE Proceedings D (Control Theory and Applications), 1985, 132(5): 225-230[17] Geromel J C, Bernussou J, Peres P L D. Decentralized control through parameter space optimization. Automatica, 1994, 30(10): 1565-1578[18] Zhao C, Zhao Y, Su H Y, Huang B. Economic performance assessment of advanced process control with LQG benchmarking. Journal of Process Control, 2009, 19(4): 557-569[19] Zhao C, Su H Y, Gu Y, Chu J. A pragmatic approach for assessing the economic performance of model predictive control systems and its industrial application. Chinese Journal of Chemical Engineering, 2009, 17(2): 241-250[20] Xu Q L, Zhao C, Zhang D F, An A M, Zhang C. Data-driven LQG benchmarking for economic performance assessment of advanced process control system. In: Proceedings of the 2011 American Control Conference. San Francisco, CA, USA: AACC, 2011. 5085-5090[21] Xu F W, Huang B, Akande S. Performance assessment of model predictive control for variability and constraint tuning. Industrial and Engineering Chemistry Research, 2007, 46(4): 1208-1219[22] Ying, C M, Joseph B. Performance and stability analysis of LP-MPC and QP-MPC cascade control systems. AIChE Journal, 1999, 45(7): 1521-1534
|