Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty

Guzman, Rel; Oliveira, Rafael S.; Ramos, Fábio

doi:10.48550/arxiv.2203.00551

Cited by 2 publications

(2 citation statements)

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“…Compared with other derivative-free optimization algorithms, such as particle swarm optimization and genetic algorithms, BO is considerably faster and more data-efficient (Lu et al, 2020). BO has been adopted in a few MPC tuning problems (Guzman et al, 2022;Lu et al, 2020;Piga et al, 2019;Sorourifar et al, 2021;Stro´_ zecki et al, 2021) to attain higher overall closed-loop performance. A review of BO can be found in Shahriari et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Tong,

Du,

Fan

et al. 2023

Transactions of the Institute of Measurement and Control

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Robust model predictive control (RMPC) is an effective technology for controlling uncertain systems while robustly handling constraints, and its closed-loop performance heavily relies on the selection of objective functions. However, the objective functions are typically chosen to be close to the real control objectives, despite an objective function that leads to less conservative constraints often provides better closed-loop performance. In this paper, we propose an automatic tuning framework for RMPC in iterative tasks. In particular, we parameterize RMPC and develop a Bayesian optimization (BO) method to tune it by solving a black-box optimization problem. We then introduce an efficient transfer learning framework within BO, which speeds up the searching process and enhances the controller performance. The effectiveness of the proposed tuning framework is illustrated on numerical examples.

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Section: Introductionmentioning

confidence: 99%

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Tong,

Du,

Fan

et al. 2023

Transactions of the Institute of Measurement and Control

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show abstract

“…Taking it into account in controller design leads to performance improvement, as demonstrated in the MPC-based controller design of robotic systems. For example, [10] and [24] account for heteroscedastic noise via an additional flexible parametric noise model. These approaches, however, utilize a restrictive model of the noise variance.…”

Section: Introductionmentioning

confidence: 99%

Safe Risk-Averse Bayesian Optimization for Controller Tuning

König,

Ozols,

Makarova

et al. 2023

IEEE Robot. Autom. Lett.

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Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been established as an efficient model-free method for controller tuning and adaptation. Standard methods, however, are not enough for high-precision systems to be robust with respect to unknown input-dependent noise and stable under safety constraints. In this work, we present a novel data-driven approach, RAGoOSe, for safe controller tuning in the presence of heteroscedastic noise, combining safe learning with risk-averse Bayesian optimization. We demonstrate the method for synthetic benchmark and compare its performance to established BO-based tuning methods. We further evaluate RaGoose performance on a real precisionmotion system utilized in semiconductor industry applications and compare it to the built-in auto-tuning routine.

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Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty

Cited by 2 publications

References 0 publications

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Safe Risk-Averse Bayesian Optimization for Controller Tuning

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