Fetching the paper…
Reading the bibliography…
The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters.
Efficient calibration of embedded MPC
Forgione, M., Piga, D., and Bemporad, A. (2019) · 1911
Earlier work this paper cites.
Structural identification by extended Kalman filter
Hoshiya, M. and Saito, E. (1984) · 1984
Earlier work this paper cites.
Regression with input-dependent noise: A Gaussian process treatment
Goldberg, P.W., Williams, C.K., and Bishop, C.M. (1998) · 1998
Earlier work this paper cites.
The sample average approximation method for stochastic discrete optimization
Kleywegt, A.J., Shapiro, A., and Homem-de Mello, T. (2002) · 2002
Earlier work this paper cites.
Gaussian processes in machine learning
Rasmussen, C.E. (2003) · 2003
Earlier work this paper cites.
Identification for control: From the early achievements to the revival of experiment design
Gevers, M. (2005) · 2005
Earlier work this paper cites.
Performance-driven cascade controller tuning with Bayesian optimization
Khosravi, M., Behrunani, V., Myszkorowski, P., Smith, R.S., Rupenyan, A., and Lygeros, J. (2020) · 2007
Earlier work this paper cites.
MPC controller tuning using Bayesian optimization techniques
Lu, Q., Kumar, R., and Zavala, V.M. (2020) · 2009
Earlier work this paper cites.
Model predictive control: Theory and design
Rawlings, J.B. and Mayne, D.Q. (2009) · 2009
Cited alongside, same era.
Model predictive control tuning methods: A review
Garriga, J.L. and Soroush, M. (2010) · 2010
Cited alongside, same era.
Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R.P. (2012) · 2012
Cited alongside, same era.
Bayesian optimization with inequality constraints
Gardner, J.R., Kusner, M.J., Xu, Z.E., Weinberger, K.Q., and Cunningham, J.P. (2014) · 2014
Cited alongside, same era.
Bayesian optimization with unknown constraints
Gelbart, M.A., Snoek, J., and Adams, R.P. (2014) · 2014
Cited alongside, same era.
Theoretical analysis of Bayesian optimisation with unknown Gaussian process hyperparameters
Wang, Z. and de Freitas, N. (2014) · 2014
Simulation optimization: A review of algorithms and applications
Amaran, S., Sahinidis, N.V., Sharda, B., and Bury, S.J. (2016) · 2016
Later among the works it cites.
Safe controller optimization for quadrotors with Gaussian processes
Berkenkamp, F., Schoellig, A.P., and Krause, A. (2016) · 2016
Later among the works it cites.
A general framework for constrained Bayesian optimization using information-based search
Hernández-Lobato, J.M., Gelbart, M.A., Adams, R.P., Hoffman, M.W., and Ghahramani, Z. (2016) · 2016
Later among the works it cites.
Goal-driven dynamics learning via Bayesian optimization
Bansal, S., Calandra, R., Xiao, T., Levine, S., and Tomiin, C.J. (2017) · 2017
Later among the works it cites.
Nonlinear model predictive control with explicit backoffs for stochastic systems under arbitrary uncertainty
Paulson, J.A. and Mesbah, A. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., and De Freitas, N. (2015) · 2015
Cited alongside, same era.
Neumann-Brosig, M., Marco, A., Schwarzmann, D., and Trimpe, S. (2019) · 2019
Later among the works it cites.
Performance-oriented model learning for data-driven MPC design
Piga, D., Forgione, M., Formentin, S., and Bemporad, A. (2019) · 2019
Later among the works it cites.