This paper presents a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored toward instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian process (GP); then, Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of codesign literature where there exists a closed-form model of the system dynamics. Furthermore, we utilize the idea of batch Bayesian optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for Altaeros' buoyant airborne turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations.
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September 2019
Research-Article
Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems
Ali Baheri,
Ali Baheri
Department of Mechanical Engineering and
Engineering Science,
University of North Carolina,
Charlotte, NC 28223
e-mail: akhayatb@uncc.edu
Engineering Science,
University of North Carolina,
Charlotte, NC 28223
e-mail: akhayatb@uncc.edu
Search for other works by this author on:
Chris Vermillion
Chris Vermillion
Department of Mechanical and
Aerospace Engineering,
North Carolina State University,
Raleigh, NC 27695;
Aerospace Engineering,
North Carolina State University,
Raleigh, NC 27695;
Search for other works by this author on:
Ali Baheri
Department of Mechanical Engineering and
Engineering Science,
University of North Carolina,
Charlotte, NC 28223
e-mail: akhayatb@uncc.edu
Engineering Science,
University of North Carolina,
Charlotte, NC 28223
e-mail: akhayatb@uncc.edu
Chris Vermillion
Department of Mechanical and
Aerospace Engineering,
North Carolina State University,
Raleigh, NC 27695;
Aerospace Engineering,
North Carolina State University,
Raleigh, NC 27695;
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received August 24, 2018; final manuscript received March 8, 2019; published online May 2, 2019. Assoc. Editor: Jongeun Choi.
J. Dyn. Sys., Meas., Control. Sep 2019, 141(9): 091013 (11 pages)
Published Online: May 2, 2019
Article history
Received:
August 24, 2018
Revised:
March 8, 2019
Citation
Baheri, A., and Vermillion, C. (May 2, 2019). "Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems." ASME. J. Dyn. Sys., Meas., Control. September 2019; 141(9): 091013. https://doi.org/10.1115/1.4043224
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