Controls and Control Theory Commons^™

On Accounting For Equipment-Control Interactions In Economic Model Predictive Control Via Process State Constraints, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Traditionally, chemical processes have been operated at steady-state; however, recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy. It is unclear how time-varying operating policies may impact process equipment, which must be investigated for safety and profit reasons. It has traditionally been considered that constraints on process states can be added to EMPC design to prevent the controller from computing control actions which create problematic operating conditions for process equipment. However, no rigorous investigation has yet been performed to analyze whether, when a …

Edge Heterogeneous Hardware Evaluation Based On Real Connected And Autonomous Vehicles (Cavs) Workloads, Mustafa Ahmad

Research Opportunities for Engineering Undergraduates (ROEU) Program 2018-19

There has recently been a wide expansion of hardware to assist in autonomous driving tasks. On this project, we focus on using some state-of-the-art deep learning workloads in connected autonomous vehicle (CAV) scenarios,such as object detection and object tracking to evaluate the heterogeneous hardware.

Three-Phase Motor Drive Using Hall Sensors And Field Oriented Control, Michael Mayberry

Research Opportunities for Engineering Undergraduates (ROEU) Program 2018-19

Permanent magnet synchronous motors (PMSM) are a promising candidate for the push to electrify transportation. They have many benefits over DC motors, however they require advanced control techniques that use accurate rotor position, to achieve optimal operation. Using Field Oriented Control (FOC) and hall sensors allows for a low-cost, reliable solution for PMSM drives.

The current study uses FOCs and hall sensors to achieve a motor drive with high power density, accurate speed control, and a small footprint.

A Nonlinear Systems Framework For Cyberattack Prevention For Chemical Process Control Systems, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Recent cyberattacks against industrial control systems highlight the criticality of preventing future attacks from disrupting plants economically or, more critically, from impacting plant safety. This work develops a nonlinear systems framework for understanding cyberattack-resilience of process and control designs and indicates through an analysis of three control designs how control laws can be inspected for this property. A chemical process example illustrates that control approaches intended for cyberattack prevention which seem intuitive are not cyberattack-resilient unless they meet the requirements of a nonlinear systems description of this property.

Economic Model Predictive Control Design Via Nonlinear Model Identification, Laura Giuliani, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an …

State Measurement Spoofing Prevention Through Model Predictive Control Design, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Security of chemical process control systems against cyberattacks is critical due to the potential for injuries and loss of life when chemical process systems fail. A potential means by which process control systems may be attacked is through the manipulation of the measurements received by the controller. One approach for addressing this is to design controllers that make manipulating the measurements received by the controller in any meaningful fashion very difficult, making the controllers a less attractive target for a cyberattack of this type. In this work, we develop a model predictive control (MPC) implementation strategy that incorporates Lyapunov-based stability …

Data-Based Nonlinear Model Identification In Economic Model Predictive Control, Laura Giuliani, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details …