882 citations · 2.6k across the 89 of their papers we have counts for
8 papers · 1 filter
Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey
Tim Martin, Thomas B. Schön, Frank Allgöwer
This survey presents recent research on determining control-theoretic properties and designing controllers with rigorous guarantees using semidefinite programming and for nonlinear…
Gaussian inference for data-driven state-feedback design of nonlinear systems
Tim Martin, Thomas B. Schön, Frank Allgöwer
Data-driven control of nonlinear systems with rigorous guarantees is a challenging problem as it usually calls for nonconvex optimization and requires often knowledge of the true b…
Data to Controller for Nonlinear Systems: An Approximate Solution
Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills +2
This paper considers the problem of determining an optimal control action based on observed data. We formulate the problem assuming that the system can be modelled by a nonlinear s…
Optimistic robust linear quadratic dual control
Jack Umenberger, Thomas B. Schon
Recent work by Mania et al. has proved that certainty equivalent control achieves nearly optimal regret for linear systems with quadratic costs. However, when parameter uncertainty…
Robust exploration in linear quadratic reinforcement learning
Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön +1
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex opti…
A fast quasi-Newton-type method for large-scale stochastic optimisation
Adrian Wills, Carl Jidling, Thomas Schon
During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improv…
Particle-based Gaussian process optimization for input design in nonlinear dynamical models
Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas +1
We propose a novel approach to input design for identification of nonlinear state space models. The optimal input sequence is obtained by maximizing a scalar cost function of the F…
A graph/particle-based method for experiment design in nonlinear systems
Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas +1
We propose an extended method for experiment design in nonlinear state space models. The proposed input design technique optimizes a scalar cost function of the information matrix,…