Optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria
- 118 Downloads
System identification is an important means for obtaining dynamical models for process control applications; experimental testing represents the most time-consuming step in this task. The design of constrained, “plant-friendly” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for data-centric estimation methods, where uniform coverage of the output state-space is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear problem example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy. The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has great benefits compared to multisine signals that minimize crest factor. The constrained nonlinear optimization problems that are solved represent challenges even for high-performance optimization software.
KeywordsSystem identification Process control Constrained optimization
Unable to display preview. Download preview PDF.
- 4.Chien, I.-L., Ogunnaike, B.A.: Modeling and control of high-purity distillation columns. In: 1992 AIChE Annual Meeting, Miami Beach, FL, paper 2a, 1992 Google Scholar
- 5.Cybenko, G.: Just-in-time learning and estimation. In: Bittani, S., Picci, G. (eds.) Identification, Adaptation, Learning. NATO ASI, pp. 423–434. Springer, Berlin (1996) Google Scholar
- 13.Lee, H., Rivera, D., Mittelmann, H.: Constrained minimum crest factor multisine signals for plant-friendly identification of highly interactive systems. In: Proceedings of the 13th IFAC Symposium on System Identification (SYSID 2003), Rotterdam, The Netherlands, pp. 947–952, 2003 Google Scholar
- 14.Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, Englewood Cliffs (1999) Google Scholar
- 15.Matoušek, J.: Geometric Discrepancy: an Illustrated Guide. Springer, Berlin (1999) Google Scholar
- 16.Mittelmann, H.: 2004, Benchmarks for optimization software. http://plato.asu.edu/bench.html
- 17.Morari, M., Zafiriou, E.: Robust Process Control. Prentice-Hall, Englewood Cliffs (1988) Google Scholar
- 18.Pendse, G.: Optimization-based formulations using the Weyl criterion for input signal design in system identification. Master’s thesis, Arizona State University, Tempe, AZ, USA (2004) Google Scholar
- 19.Rivera, D., Lee, H., Braun, M., Mittelmann, H.: Plant-friendly system identification: a challenge for the process industries. In: Proc. of the 13th IFAC Symposium on System Identification (SYSID 2003), Rotterdam, Netherlands, pp. 917–922, 2003 Google Scholar
- 21.Stenman, A.: Model on demand: algorithms, analysis and applications. Ph.D. thesis, Department of Electrical Engineering, Linköping University, Sweden (1999) Google Scholar