Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlVariational InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competin…
Variational InferenceBayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control
This paper presents an auto-tuning framework for torque-based Nonlinear Model Predictive Control (nMPC), where the MPC serves as a real-time controller for optimal joint torque commands. The MPC parameters, including cos…
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP appro…
regressionStochastic OptimizationEfficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
Sparse deep learning aims to address the challenge of huge storage consumption by deep neural networks, and to recover the sparse structure of target functions. Although tremendous empirical successes have been achieved,…
Deep LearningUncertainty QuantificationVariable SelectionVariational InferenceBayesian Learning Approach to Model Predictive Control
This study presents a Bayesian learning perspective towards model predictive control algorithms. High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model p…
modelModel Predictive Control