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Found 18 papers, 4 papers with code
Stability-informed Bayesian Optimization for MPC Cost Function Learning
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging.
Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging
Tuning parameters in model predictive control (MPC) presents significant challenges, particularly when there is a notable discrepancy between the controller's predictions and the actual behavior of the closed-loop plant.
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
Prediction of battery cycle life and estimation of aging states is important to accelerate battery R&D, testing, and to further the understanding of how batteries degrade.
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models
We implement optimal control constrained by knowledge-based and hybrid models for optogenetic ATPase expression in $\textit{Escherichia coli}$ with lactate as the main product.
Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees
We address the challenge of dimension reduction in the discrete-time optimal control problem which is solved repeatedly online within the framework of model predictive control.
Regret and Conservatism of Distributionally Robust Constrained Stochastic Model Predictive Control
Furthermore, we quantify the regret by comparing the performance when the distributions of the stochastic uncertainties are known and unknown.
Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data
We developed an optimization formulation to compare regression coefficients and coefficients obtained by physical engineering knowledge to understand which part of the coefficient differences are close to the nullspace.
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics
The model predictive controller uses these Gaussian process models to achieve precise motion and force control under stochastic constraint satisfaction.
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior.
LMI-based Data-Driven Robust Model Predictive Control
Predictive control, which is based on a model of the system to compute the applied input optimizing the future system behavior, is by now widely used.
Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra
Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data.
Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics
Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances.
Cultural Vocal Bursts Intensity Prediction
Model Predictive Control
Machine Learning for Process Control of (Bio)Chemical Processes
The control of manufacturing processes must satisfy high quality and efficiency requirements while meeting safety requirements.
Safe Hierarchical Model Predictive Control and Planning for Autonomous Systems
Planner and controller are based on the repeated solution of moving horizon optimal control problems.
Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC
Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used for decades in many engineering applications.
Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
Optimal control under uncertainty is a prevailing challenge for many reasons.
Learning supported Model Predictive Control for Tracking of Periodic References
Increased autonomy of controllers in tasks with uncertainties stemming from the interaction with the environment can be achieved by incorporation of learning.
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees
Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction.
