(257a) Perspectives on the Control of Advanced Manufacturing Systems

Several countries and companies in recent years have become interested in advanced manufacturing, which is the highly efficient, effective, and reliable manufacturing of complex products to satisfy tight specifications. Such complex products typically are composite solid materials manufactured in over a dozen unit operations that require precise control over a very wide range of time and length scales, with many of the unit operations involving one or more solids. An example of a complex product is a pharmaceutical tablet that requires tight control of molecular purity, crystal structure, crystal size distribution, and the long-term chemical stability of the tablet when the active pharmaceutical ingredient is compressed with excipients [e.g., see Refs. 1-2 and citations therein].

Advanced manufacturing systems have many characteristics that break existing control theories, which include (1) high to infinite state dimension, (2) probabilistic parameter uncertainties, (3) time delays, (4) unstable zero dynamics, (5) actuator, state, and output constraints, (6) noise and disturbances, and (7) phenomena described by combinations of algebraic, ordinary differential, partial differential, and integral equations (that is, generalizations of descriptor/singular systems). While control theories have been developed for systems that have some of these characteristics, attempting to address all of these characteristics results in computational costs that are too high to be implemented, a lack of theoretical guarantees, and/or conservatism.

This presentation provides some perspectives on the analysis and design of control systems that satisfy all of the characteristics of advanced manufacturing systems. Model predictive control (MPC) formulations are reviewed that have the flexibility to handle dynamical systems with these characteristics by employing polynomial chaos theory and projections. The MPC formulations have low sensitivities to model uncertainties and low online computational cost, and have been demonstrated in realistic simulations of advanced manufacturing systems. Some promising directions are proposed for future research.

References

1. R. Lakerveld, P. L. Heider, K. D. Jensen, R. D. Braatz, K. F. Jensen, A. S. Myerson, and B. L. Trout. End-to-end continuous manufacturing: Integration of unit operations. In Continuous Manufacturing of Pharmaceuticals, edited by P. Kleinebudde, J. Khinnast, and J. Rantanen, Wiley, New York, Chapter 13, pages 447-483, 2017.
2. A. T. Myerson, M. Krumme, M. Nasr, H. Thomas, and R. D. Braatz. Control systems engineering in continuous pharmaceutical processing. Journal of Pharmaceutical Sciences, 104(3):832-839, 2015.