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We present the Adaptive Approximate Dynamic Optimization (AADO) algorithm and elaborate on its application for forward (dynamics as constraints) and inverse (dynamics in the objective) Dynamic Optimization (DO). The method is tailored to dynamic optimization problems where the system model contains high-fidelity (HF) model components of subsystem(s) that slow down the system model's evaluation time. In this contribution we extend prior work where we proposed to replace the system model with a local (i.e. only valid in subregions of the state space) approximate dynamic system model based on sampled model evaluations. The sampling is adaptive and localized to the optimal trajectory in correspondence with the trajectory optimizer. Here we suggest only to replace the high-fidelity subsystem models with an approximate subsystem model set. A comparison is made of the method applied in both the forward and inverse convention on a limit cycle slider-crank trajectory optimization for a high-fidelity nonlinear load model. We found that the AADO algorithm requires only 0.1% system model evaluations compared with conventional DO and only 30% compared with non-adaptive AADO. Our results consolidate the methods potential and invite for further research.
Keywords
SEQUENTIAL DESIGN STRATEGY

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MLA
Lefebvre, Tom, et al. “Adaptive Approximate Dynamic Optimization : A Slider-Crank Case Study.” 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, 2019, pp. 1509–15, doi:10.1109/aim.2019.8868815.
APA
Lefebvre, T., De Belie, F., Naets, F., & Crevecoeur, G. (2019). Adaptive approximate dynamic optimization : a slider-crank case study. 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 1509–1515. https://doi.org/10.1109/aim.2019.8868815
Chicago author-date
Lefebvre, Tom, Frederik De Belie, Frank Naets, and Guillaume Crevecoeur. 2019. “Adaptive Approximate Dynamic Optimization : A Slider-Crank Case Study.” In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 1509–15. New York: IEEE. https://doi.org/10.1109/aim.2019.8868815.
Chicago author-date (all authors)
Lefebvre, Tom, Frederik De Belie, Frank Naets, and Guillaume Crevecoeur. 2019. “Adaptive Approximate Dynamic Optimization : A Slider-Crank Case Study.” In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 1509–1515. New York: IEEE. doi:10.1109/aim.2019.8868815.
Vancouver
1.
Lefebvre T, De Belie F, Naets F, Crevecoeur G. Adaptive approximate dynamic optimization : a slider-crank case study. In: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). New York: IEEE; 2019. p. 1509–15.
IEEE
[1]
T. Lefebvre, F. De Belie, F. Naets, and G. Crevecoeur, “Adaptive approximate dynamic optimization : a slider-crank case study,” in 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, HONG KONG, 2019, pp. 1509–1515.
@inproceedings{8634350,
  abstract     = {{We present the Adaptive Approximate Dynamic Optimization (AADO) algorithm and elaborate on its application for forward (dynamics as constraints) and inverse (dynamics in the objective) Dynamic Optimization (DO). The method is tailored to dynamic optimization problems where the system model contains high-fidelity (HF) model components of subsystem(s) that slow down the system model's evaluation time. In this contribution we extend prior work where we proposed to replace the system model with a local (i.e. only valid in subregions of the state space) approximate dynamic system model based on sampled model evaluations. The sampling is adaptive and localized to the optimal trajectory in correspondence with the trajectory optimizer. Here we suggest only to replace the high-fidelity subsystem models with an approximate subsystem model set. A comparison is made of the method applied in both the forward and inverse convention on a limit cycle slider-crank trajectory optimization for a high-fidelity nonlinear load model. We found that the AADO algorithm requires only 0.1% system model evaluations compared with conventional DO and only 30% compared with non-adaptive AADO. Our results consolidate the methods potential and invite for further research.}},
  author       = {{Lefebvre, Tom and De Belie, Frederik and Naets, Frank and Crevecoeur, Guillaume}},
  booktitle    = {{2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}},
  isbn         = {{9781728124933}},
  issn         = {{2159-6255}},
  keywords     = {{SEQUENTIAL DESIGN STRATEGY}},
  language     = {{eng}},
  location     = {{Hong Kong, HONG KONG}},
  pages        = {{1509--1515}},
  publisher    = {{IEEE}},
  title        = {{Adaptive approximate dynamic optimization : a slider-crank case study}},
  url          = {{http://doi.org/10.1109/aim.2019.8868815}},
  year         = {{2019}},
}

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