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Regularized System Identification: A Hierarchical Bayesian Approach
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsIn this paper, the hierarchical Bayesian method for regularized system identification is introduced. To this end, a hyperprior distribution is considered for the regularization matrix and then, the impulse response and the regularization matrix are jointly estimated based on a maximum a posteriori (MAP) approach. Toward introducing a suitable hyperprior, we decompose the regularization matrix using Cholesky decomposition and reduce the ...Conference Paper -
Low-Complexity Identification by Sparse Hyperparameter Estimation
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsThis paper presents a novel kernel-based system identification method, which promotes low complexity of the model in terms of the McMillan degree of the system. The regularization matrix is characterized as a linear combination of pre-selected rank-one matrices with unknown hyperparameter coefficients, and the hyperparameters are derived using a maximum a posteriori estimation approach. Each basis matrix is the optimal regularization ...Conference Paper -
Robust Adaptive Model Predictive Control with Worst-Case Cost
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsA robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. Online set-membership identi cation is used to reduce uncertainty in the impulse response. In the MPC scheme, robust constraints are ...Conference Paper -
Structured exploration in the finite horizon linear quadratic dual control problem
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsThis paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty. To this end, a synthesis problem is defined where the feedback law has to simultaneously gain knowledge of the system and robustly optimize the cost. By framing the problem in a finite horizon setting, the trade-offs arising when the tasks include both ...Conference Paper -
Robust Control of a Lightweight Structure for Digital Fabrication
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsFor a material efficient construction process of lightweight concrete shells, tensioned cablenets can be used as a supporting formwork. In order to guarantee the mechanical stability of the shells,tight tolerances in their form need to be met. To this end, methods have recently been proposed to readjustthe form of the cable net on the construction site. This paperproposes a novel view on the cable netmodel as a dynamical system and derives ...Conference Paper -
Experiments and identification of thermoacoustic instabilities with the Rijke tube
(2020)2020 IEEE Conference on Control Technology and Applications (CCTA)This paper is concerned with the experimental characterization of a thermoacoustic demonstrator known as the Rijke tube. An approach which leverages the prior qualitative knowledge of the sys-tem's behaviour to plan the experiment campaign and identify reliable models of the system is described. First, nonlinear features of the dynamics, e.g. the type of Hopf bifurcation triggered by the thermoacoustic coupling and the periodic orbits ...Conference Paper -
Feedback Control Design Maximizing the Region of Attraction of Stochastic Systems Using Polynomial Chaos Expansion
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsA feedback control design is proposed for stochastic systems with finite second moment which aims at maximising the region of attraction of the equilibrium point. Polynomial Chaos (PC) expansions are employed to represent the stochastic closed loop system by a higher dimensional set of deterministic equations. By using the PC expanded system representation, the available information on the uncertainty affecting the system explicitly enters ...Conference Paper -
Regularized classification and simulation of bifurcation regimes in nonlinear systems
(2021)IFAC-PapersOnLineThe paper proposes a multi-step identification approach to classify a nonlinear system into qualitatively different regimes and then estimate a low-dimensional subspace where predictions of the original state at future times can be obtained by simulation of low-order dynamics. Proper Orthogonal Decomposition is used to build a library of characteristic modes from training data and is combined with regularization techniques for both the ...Conference Paper -
Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsThis paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the ...Conference Paper -
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
(2021)Proceedings of Machine Learning Research ~ Proceedings of the 3rd Conference on Learning for Dynamics and ControlThe paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can ...Conference Paper