Nonlinear Sparse Variational Bayesian Learning-Based Model Predictive Control for Nonlinear Systems with Application to PEMFC Temperature Control

Core Concepts

The study develops a nonlinear sparse variational Bayesian learning-based model predictive control (NSVB-MPC) approach to improve data-driven control of nonlinear systems. The NSVB method learns the model by employing variational inference to assess predictive accuracy and quantify system uncertainty, ensuring input-to-state stability and feasibility of recursive constraints.

Abstract

The paper presents a nonlinear sparse variational Bayesian learning-based model predictive control (NSVB-MPC) approach for controlling unknown discrete-time nonlinear systems.
Key highlights:

The NSVB method is developed to learn the model from input-output data, employing sparsity-inducing priors and variational inference to assess predictive accuracy and quantify system uncertainty.
The NSVB-MPC approach ensures input-to-state stability (ISS) and feasibility of recursive constraints without the need for robust invariant region estimation.
The effectiveness of the NSVB-MPC method is demonstrated through a PEMFC temperature control case study.
The paper first formulates the problem of controlling an unknown nonlinear system with input and output constraints. It is assumed the system can be characterized by a nonlinear autoregressive exogenous (NARX) model.
The core of the paper is the development of the NSVB learning method:

Bayesian inference is used to select the optimal model structure, with automatic relevance determination (ARD) pruning redundant model terms.
Variational inference is employed to quantify parameter uncertainty, modeling the predictive distribution as a probability distribution.
Closed-form update equations are derived for the optimal variational distributions of the model parameters (weights, hyperparameters, noise precision).
The evidence lower bound (ELBO) is maximized to find the optimal approximate posterior distribution, guaranteeing convergence.
For the control phase, the NSVB-MPC approach is designed to ensure robust stability under constraints without the need for terminal constraints or robust invariant region estimation. The control law inherits the input-to-state stability (ISS) property of the nominal MPC under constraints.
The effectiveness of the NSVB-MPC method is demonstrated through a PEMFC temperature control case study, confirming its ability to learn accurate models and provide stable control for nonlinear systems.

Stats

The paper does not provide specific numerical data or metrics, but focuses on the theoretical development of the NSVB-MPC approach and its application to PEMFC temperature control.

Quotes

"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."
"Sparse Bayesian learning (SBL) methods provide a principled approach to address the challenges of complex measurement data, overfitting, and lack of interpretability."

Key Insights Distilled From

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

by Qi Zhang,Lei... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09519.pdf

Deeper Inquiries

How can the NSVB-MPC approach be extended to handle time-varying or uncertain constraints on the input and output sequences

To extend the NSVB-MPC approach to handle time-varying or uncertain constraints on the input and output sequences, the model predictive control (MPC) formulation can be modified to incorporate these variations. Time-varying constraints can be included by updating the constraints at each time step based on the changing system conditions. Uncertain constraints can be addressed by introducing probabilistic constraints that capture the uncertainty in the system dynamics. By utilizing Bayesian inference and variational methods, the NSVB-MPC framework can adapt to these variations by updating the model parameters and constraints based on the available data and uncertainty estimates. This adaptive approach allows the controller to adjust its predictions and control actions in real-time to accommodate changing constraints, improving the robustness and performance of the control system.

What are the potential limitations of the NSVB-MPC method in terms of scalability and computational complexity as the system dimensionality increases

The NSVB-MPC method may face limitations in scalability and computational complexity as the system dimensionality increases. As the number of states, inputs, and outputs in the system grows, the size of the model and the number of parameters to be learned also increase, leading to higher computational demands. The sparse Bayesian learning approach used in NSVB-MPC relies on iterative optimization and inference procedures, which can become computationally intensive for large-scale systems. Additionally, the storage and manipulation of large datasets and high-dimensional parameter spaces can pose challenges in terms of memory usage and computational efficiency. To address these limitations, advanced optimization techniques, parallel computing strategies, and model reduction methods can be employed to enhance the scalability and computational performance of the NSVB-MPC framework for high-dimensional systems.

Can the NSVB-MPC framework be adapted to incorporate additional prior knowledge or domain-specific information about the nonlinear system dynamics

The NSVB-MPC framework can be adapted to incorporate additional prior knowledge or domain-specific information about the nonlinear system dynamics by integrating expert insights or physical constraints into the model learning and control design process. Prior knowledge about the system dynamics, such as known relationships between variables, structural properties, or constraints, can be encoded into the model structure or parameter priors to guide the learning process. Domain-specific information, such as system behavior under certain operating conditions or known patterns in the data, can be utilized to improve the accuracy and generalization of the learned models. By combining data-driven learning with domain knowledge, the NSVB-MPC approach can leverage the strengths of both approaches to enhance the control performance, interpretability, and adaptability of the system. This integration of prior knowledge can help in reducing the data requirements, improving model robustness, and providing more meaningful insights for control decisions.