Search Results for author:
Found 15 papers, 0 papers with code
MPC using mixed-integer programming for aquifer thermal energy storages
Aquifer thermal energy storages (ATES) are used to temporally store thermal energy in groundwater saturated aquifers.
A code-driven tutorial on encrypted control: From pioneering realizations to modern implementations
The growing interconnectivity in control systems due to robust wireless communication and cloud usage paves the way for exciting new opportunities such as data-driven control and service-based decision-making.
Extending direct data-driven predictive control towards systems with finite control sets
We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.
Towards a unifying framework for data-driven predictive control with quadratic regularization
Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC).
Privacy Analysis of Affine Transformations in Cloud-based MPC: Vulnerability to Side-knowledge
In addition, while we prove that outsourcing the MPC problem in the dense form inherently leads to some degree of privacy for the system and cost function parameters, we also establish that affine transformations applied to this form are nevertheless prone to be undermined by a Cloud with mild side-knowledge.
On the security of randomly transformed quadratic programs for privacy-preserving cloud-based control
Control related data, such as system states and inputs or controller specifications, is often sensitive.
Implicit predictors in regularized data-driven predictive control
We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as in traditional model or subspace predictive control).
Error bounds for maxout neural network approximations of model predictive control
However, such guarantees can be recovered if the maximum error with respect to the optimal control law and the Lipschitz constant of that error are known.
Convex NMPC reformulations for a special class of nonlinear multi-input systems with application to rank-one bilinear networks
We show that a special class of (nonconvex) NMPC problems admits an exact solution by reformulating them as a finite number of convex subproblems, extending previous results to the multi-input case.
Encrypted extremum seeking for privacy-preserving PID tuning as-a-Service
Wireless communication offers many benefits for control such as substantially reduced deployment costs, higher flexibility, as well as easier data access.
Convex reformulations for a special class of nonlinear MPC problems
We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control problem in terms of a finite number of convex subproblems.
Tailored max-out networks for learning convex PWQ functions
In this context, a recurring question is how to choose the topology of the NN in terms of depth, width, and activations in order to enable efficient learning.
A deterministic view on explicit data-driven (M)PC
We show that the explicit realization of data-driven predictive control (DPC) for linear deterministic systems is more tractable than previously thought.
Tailored neural networks for learning optimal value functions in MPC
In this paper, we provide a similar result for representing the optimal value function and the Q-function that are both known to be piecewise quadratic for linear MPC.
Novel convex decomposition of piecewise affine functions
In this paper, we present a novel approach to decompose a given piecewise affine (PWA) function into two convex PWA functions.
