Brian Bole – ICSL

Brian2.png Office:
Room 227
Intelligent Control Systems Laboratory (ICSL)
Manufacturing Research Center (MaRC)
Georgia Institute of Technology (GaTech)
813 Ferst Drive, N.W.
Atlanta, Georgia 30332-0560 U.S.A.

Telephone: +1(404) 894-4132

Curriculum Vitae: (Download PDF)


I graduated from the FSU-FAMU School of Engineering in 2008 with a B.S. in ECE and a B.S. in Applied Math. I am currently pursuing a Ph.D. in Electrical and Computer Engineering at the Georgia Institute of Technology. My research interests include prognostics based reconfigurable control, risk assessment, and stochastic optimization.


Uncertainty Representation for Prognostic Modeling

The form of prognostic uncertainty will depend on the particular fault mode being studied, system modeling uncertainty, measurement uncertainty, and uncertainty regarding the future loads on the system. If we had access to a prognostic model that would translate an expected load on a component over the prognostic horizon into a pdf estimate of component degradation at the end of the prognostic horizon, then we have the means to fully analyze risk. For simple systems, empirical degradation models may be used as a basis for producing prognostic pdfs. For example, Paris’ Law is commonly used to update pdfs for crack growth.

Evaluation of risk metrics based on prognostic pdfs is an area of current study. The definition of risk is to some extent based on subjective reasoning and will very widely across applications, so there is a tremendous need to define rigorous methodologies for specifying risk metrics.

Optimal Load Allocation in Overactuated Systems

Assuming that a probabilistic prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component loads to minimize risk of failure, while not overly degrading nominal performance. A methodology is proposed for posing this problem as a finite horizon constrained optimization, where constraints correspond to maximum risk of failure and maximum deviation from nominal performance.

Application Systems for Active Contingency Management (ACM)

Electro-mechanical actuator (EMA): Consists of three DC motors geared to the same output shaft. Similar actuator systems are commonly used in aerospace, transportation, and industrial processes to actuate critical loads, such as aircraft control surfaces. The fault mode simulated in the system is a temperature dependent motor winding insulation degradation. The simple active redundant configuration of effectors in this system make it well suited to demonstrate the fundamental properties of load allocation for risk management.

AirSTAR (Airborne Subscale Transport Aircraft Research) UAV: NASA Langley has constructed a 5.5% scale Boeing 757 UAV, which is being used to conduct rapid prototyping of control algorithms that operate in extreme flight conditions. NASA has provided us with a complete nonlinear model of the UAV and we have begun preliminary development of fault tolerant control routines, which would redistribute load among available control surfaces in the UAV and make slight adjustments to its flight profile based on prognostic information.


(EcoCAR)is a three year competition, sponsored by General Motors and the US Department of Energy, to develop a practical and environmentally friendly SUV. I am working with a small team of students and faculty to develop and implement a supervisory controller for the hybrid electric vehicle.

The vehicle used is a 2009 Saturn Vue with a FWD GM 2-Mode Hybrid Transmission and 10 KWh battery. We have worked on the creation of evaluation metrics to specify an optimization problem to be solved by the supervisory controller. We are currently pursuing stochastic optimization methods to solve an optimal power distribution problem, where the driver’s pedal inputs are viewed as the stochastic variable.


Bole, B., Goebel, K., Vachtsevanos, G. “Markov Modeling of Component Fault Growth Over a Derived Domain of Feasible Output Control Effort Modifications” Annual Conference of the Prognostics and Health Management Society, 2012. (Download PDF)
Bole, B., Goebel, K., Vachtsevanos, G. “Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions” The American Institute of Aeronautics and Astronautincs (AIAA) Infotech Conference, 2012. (Download PDF)
Bole, B., Coogan, S., Cubero-Ponce, C., Edwards, D., Melsert, R., and Taylor, D., “Energy Management Control of a Hybrid Electric Vehicle with Two-Mode Electrically Variable Transmission” The 26th Annual Electric Vehicle Symposium EVS26, 2012. (Download PDF)
Bole, B., Tang, L., Goebel, K., and Vachtsevanos, G. “Adaptive Load-Allocation for Prognosis-Based Risk Management,” Annual Conference of the Prognostics and Health Management Society 2011. (Download PDF)
Bole, B., Brown, D., Pei, H.-L., Goebel, K., Tang, L., and Vachtsevanos, G. “Fault Adaptive Control of Overactuated Systems Using Prognostic Estimation” Annual Conference of the Prognostics and Health Management Society, 2010. (Download PDF)
Bole, B., Brown, D., Pei, H.-L., Goebel, K., Tang, L., and Vachtsevanos, G. ““Load allocation for Risk Management in Overactuated Systems Experiencing Incipient Failure Conditions” Control and Fault-Tolerant Systems (SysTol), 2010. (Download PDF)
Bole, B., Brown, D., Vachtsevanos, G., “Automated Contengency Management (ACM) for Overactuated Systems” MFPT 2010, Transition: From R&D to Product, pg 277-288, 2010.
Brown, D., Bole, B., and Vachtsevanos, G. “A Prognostics Enhanced Reconfigurable Control Architecture” 18th Mediterranean Conference on Control & Automation, 2010.

(Download PDF)

Brown, D., Georgoulas, G., Bole, B., Pei, H., Orchard, M., Tang, L., Bhaskar, S., Saxena, A., Goebel, K., and Vachtsevanos, G. “Prognostics Enhanced Reconfigurable Control of Electro-Mechanical Actuators” 2nd International Conference on Prognostics and Health Management (PHM), 2009.

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Choi, C., Block, I., Bole, B., Dralle, D., Cunningham, B., “Label-Free Photonic Crystal Biosensor Integrated Microfluidic Chip for Determination of Kinetic Reaction Rate Constants” IEEE sensors journal, 2009.

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