Towards embedded model predictive control for System-on-a-Chip applications

Leonidas G. Bleris, Jesus Garcia, Mayuresh V. Kothare, Mark G. Arnold

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


We propose a framework for embedding model predictive control for Systems-on-a-Chip applications. In order to allow the implementation of such a computationally expensive controller on chip, we propose reducing the precision of the microprocessor to the minimum while maintaining near optimal control performance. Taking advantage of the low precision, a logarithmic number system based microprocessor architecture is used, that allows the design of a reduced size processor, providing further energy and computational cost savings. The design parameters for this high-performance embedded controller are chosen using a combination of finite element method simulations and bit-accurate hardware emulations in a number of parametric tests. We provide the methodology for choosing the design parameters for two particular control problems; the temperature regulation in a wafer cross-section geometry, and the control of temperature in a non-isothermal fluid flow problem in a microdevice. Finally, we provide the microprocessor architecture details and estimates for the performance of the resulting embedded model predictive controller.

Original languageEnglish (US)
Pages (from-to)255-264
Number of pages10
JournalJournal of Process Control
Issue number3
StatePublished - Mar 2006
Externally publishedYes


  • Embedded model predictive control
  • Microchemical systems
  • Reduced precision microprocessors
  • Systems-on-a-Chip

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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