OUR RESEARCH FOCUSES ON THE ANALYSIS AND DESIGN of advanced multivariable control systems. Our approach is to establish new theoretical foundations and validate advances through computer simulation studies and experimental implementations. The applications include energy production systems and fuel cells, the manufacture of integrated microelectronic and photovoltaic devices, control of autonomous vehicles, and the development of on-line measurement instrumentation, among other fields of interest.
CONTROL SCIENCE We design controllers that deliver high performance in spite of the presence of modeling uncertainty. Ongoing research seeks the synthesis of robust multivariable controllers such as predictive-control, variable-structure control, and frequency-domain techniques, including our formulation of the Nyquist Robust Stability Margin as a robustness metric.
VIRTUAL SENSORS Often critical process variables needed for diagnostics and control cannot be measured because of the inability to place a physical sensor inside constrained geometries. Our group designs software sensors that estimate the value of inaccessible measurements using mathematical models and data from other locations. The technology involves Kalman and Luenberger observers, as well as integral observers that can preserve accuracy even under conditions of data uncertainty.
FUEL CELLS We are developing direct methanol fuel cells designed to serve as long lasting power supplies for small electrical appliances. Our group conducts first-principles fuel cell modeling work to serve as the basis for designing real-time control manipulations. The objective is to optimize operations and ensure high quality performance. The effort seeks to contribute new green and renewable energy production technologies that can effectively address our society’s growing need for a sustainable energy infrastructure.
Ph.D., 1990, University of California-Santa Barbara
Awards & Distinctions
National Science Foundation CAREER Award
- Multimodal Multi-objective Optimization with Differential Evolution”, by Caitong Yue, Weiwei Xu, Hui Song, Jing Liang, and Oscar D. Crisalle, Swarm and Evolutionary Computation (to appear, 2018).
- “Analysis of Retailers’ Coalition Stability for Supply Chain Based on LCS and Stable Set”, by Yong Luo, Shi-Zhao Wang, Xiao-Chen Sun, and Oscar D. Crisalle, International Journal of Production Research, pp. 170-175 (2016).
- “Comprehensive Mass Transport Modeling Technique for the Cathode Side of an Open-Cathode Direct Methanol Fuel Cell”, by Shyam P. Mudiraj, M.A.R. Biswas, William E. Lear, and Oscar D. Crisalle, International Journal of Hydrogen Energy, Vol. 40, No. 25, pp. 8137-8159, (2015).
- “Two-Hidden-Layer Extreme Learning Machine for Regression and Classification”, by B.Y. Qu, B.F. Lang, J.J. Liang, A.K. Qin, and O.D. Crisalle, Neurocomputing, Vol. 175, pp, 826-834 (2016).
- “Systematic Approach for Modeling Methanol Mass Transport on the Anode Side of Direct Methanol Fuel Cells”, by M. A. R. Biswas, Shyam P. Mudiraj, W.E. Lear, and Oscar D. Crisalle, International Journal of Hydrogen Energy, Vol. 39, No. 15, pp. 8009-8025, (2014).
- Al-Shamali, S.A. Ji, B., Crisalle, O.D., and Latchman, H.A., “The Nyquist Robust Sensitivity Margin for Uncertain Closed-Loop Systems,” International Journal of Robust and Nonlinear Control, 15, (2005) 619.