SBIR-STTR Award

The difficult problems being tackled in the accelerator community are those that are nonlinear and substantially unmodeled. So, those problems now seem ideal candidates for model-predictive-control technology, especially for implementations in which the nonlinear aspects can be modeled and the real system can be identified online. In general, deploying and maintaining a model is the most expensive part of implementing a nonlinear model-predictive-control system, and any technology with adaptive control that reduces model development time is of significant economic value. The proposed project will search for a novel way to include adaptive control in a nonlinear model-predictive-control algorithm. In Phase I, there will be a thorough feasibility study of how to develop the use of novel adaptive, nonlinear controls on two challenging problems in the electron and positron storage rings at a national laboratory that have a direct impact on luminosity and beam lifetime that are factors influencing the research efficiency in such a facility. Commercial Applications and Other Benefits as directed by the awardee: This research enables a commercial party to leverage the knowledge gained through collaboration with a national laboratory to develop a new model-predictive-control product with nonlinear, adaptive-control capabilities to address current and future needs in process industries.

Variation in system dynamics and modeling uncertainty (due to unmodeled system behavior and/or presence of disturbances), have posed significant challenges to the effective luminosity and orbit control in accelerators used in nuclear physics research. Adaptive control has long been pursued as a possible solution, but difficulties with online model identification, and robust implementation of the adaptive control algorithms has prevented their widespread application. In addition, the performance of the control system is contingent on the responsiveness of the control algorithm to the inevitable deviations of the model from the actual system. This project will use neural networks to detect significant changes in system behavior and develop the methodology for online identification of new empirical models. Furthermore, an optimal model-predictive-based adaptive control algorithm will be developed, which enables the robust implementation of an effective control strategy. In Phase I, simulations were conducted to clearly demonstrate the feasibility and benefits of implementing model predictive control technology in accelerator control problems. In addition, a prototype for the optimal model-predictive-based adaptive control algorithm was developed for a well-known nonlinear temperature control problem for gas-phase reactors. In Phase II, a classification algorithm for dynamic data will be developed to enable the detection of significant changes in system behavior. Algorithms for efficient handling of variable dynamics in the nonlinear model predictive control system will be developed, and the machinery that allows the implementation of the optimal adaptive schemes will be put in place. Prototypes to implement the above-mentioned features in commercially available software will be developed.

Commercial Applications and Other Benefits as described by the awardee:
The online system identification and optimal model-predictive-based adaptive control software should have applicability in process industries, power systems, and financial systems. In particular, the day-to-day operation of accelerators should immediately benefit from the findings in this project.