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Found 10 papers, 1 papers with code
Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements.
From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met.
Active Learning-based Model Predictive Coverage Control
In addition, we derive a control framework that avoids the hierarchical structure by integrating the reference optimization in the MPC formulation.
Stochastic MPC with robustness to bounded parametric uncertainty
assumption on the noise distribution, we also provide an average asymptotic performance bound for the l2-norm of the closed-loop state.
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus control performance.
Distributed Model Predictive Safety Certification for Learning-based Control
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies.
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data.
Multi-agents adaptive estimation and coverage control using Gaussian regression
We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function.
