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Found 16 papers, 2 papers with code
Exploring the Links between the Fundamental Lemma and Kernel Regression
Generalizations and variations of the fundamental lemma by Willems et al. are an active topic of recent research.
The Dynamics of a Bicycle on a Pump Track -- First Results on Modeling and Optimal Control
We investigate the dynamics of a bicycle on an uneven mountain bike track split into straight sections with small jumps (kickers) and banked corners.
Distributionally robust uncertainty quantification via data-driven stochastic optimal control
This paper studies optimal control problems of unknown linear systems subject to stochastic disturbances of uncertain distribution.
Quantized Deep Path-following Control on a Microcontroller
Model predictive Path-Following Control (MPFC) is a viable option for motion systems in many application domains.
Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations
We discuss the implementation via decentralized optimization algorithms.
Towards data-driven stochastic predictive control
Data-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise.
Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions
The stochastic variant of the lemma is applicable to a large class of linear dynamics subject to stochastic disturbances of Gaussian and non-Gaussian nature.
On Data-Driven Stochastic Output-Feedback Predictive Control
Building on a stochastic variant of the fundamental lemma, this paper presents a data-driven output-feedback predictive control scheme for stochastic Linear Time-Invariant (LTI) systems.
Approximations for Optimal Experimental Design in Power System Parameter Estimation
This paper is about computationally tractable methods for power system parameter estimation and Optimal Experiment Design (OED).
On a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control
Data-driven control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise.
Essentially Decentralized Conjugate Gradients
Solving structured systems of linear equations in a non-centralized fashion is an important step in many distributed optimization and control algorithms.
Distributed Optimization
Optimization and Control
Systems and Control
Systems and Control
On the Turnpike to Design of Deep Neural Nets: Explicit Depth Bounds
It is well-known that the training of Deep Neural Networks (DNN) can be formalized in the language of optimal control.
Turnpike Properties in Optimal Control: An Overview of Discrete-Time and Continuous-Time Results
The turnpike property refers to the phenomenon that in many optimal control problems, the solutions for different initial conditions and varying horizons approach a neighborhood of a specific steady state, then stay in this neighborhood for the major part of the time horizon, until they may finally depart.
Optimization and Control Systems and Control Systems and Control
Online power system parameter estimation and optimal operation
The integration of renewables into electrical grids calls for optimization-based control schemes requiring reliable grid models.
ALADIN-$α$ -- An open-source MATLAB toolbox for distributed non-convex optimization
This paper introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-$\alpha$.
PolyChaos.jl -- A Julia Package for Polynomial Chaos in Systems and Control
With PolyChaos we provide a Julia software package that delivers the desired functionality: given a probability density function, PolyChaos offers several numerical routines to construct the respective orthogonal polynomials, and the quadrature rules together with tensorized scalar products.
Systems and Control Numerical Analysis Systems and Control Numerical Analysis Optimization and Control
