From experiment design to closed-loop control

By Håkan Hjalmarsson. at - Automatisierungstechnik, 2005

555 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/83b8da41917b7859b28956db94c6e289098e3c48

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In Control and Systems Engineering

Citations in the corpus

A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems (2011, methodology): “For details about the IFT method, please refer to [21]–[24].”
Bridging Direct and Indirect Data-Driven Control Formulations via Regularizations and Relaxations (2021, methodology): “As a vehicle to transition between indirect and direct approaches, we consider a multicriteria problem trading off identification and control objectives reminiscent of similar approaches [7], [12]–[20] blending the two.”
Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems (2011, background): “IFT is also suggested for an unknown discrete-time SISO LTI system [19]–[23].”
Identification for Control: From the Early Achievements to the Revival of Experiment Design (2005, background): “For a superb presentation of the technical issues in identification for control, we refer to [37], which is compelling reading for anyone who aims at becoming”
Optimal instrumental variable method for closed-loop identification (2011, background): “Renewed attention has been given lately to the problem of closed-loop identification initiated by an emerging interest in identification for model-based control ([3], [10], [7]) and/or experiment design in the closed-loop framework ([15],[8]).”
A new kernel-based approach for linear system identification (2010, background): “Key words: linear system identification; kernel-based methods; Bayesian estimation; regularization; Gaussian processes; robust identification; stochastic embedding”
Refined Instrumental Variable methods for closed-loop system identification (2009, unknown): “(Söderström and Stoica, 1989), (Van den Hof, 1998), (Forssell and Chou, 1998), (Forssell and Ljung, 1999), (Zheng, 2001), (Zheng, 2003), (Gevers, 2005), (Ninness and Hjalmarsson, 2005), (Hjalmarsson, 2005), (Agüero and Goodwin, 2007), (Mǐsković et al.”
Data‐driven model reference control with asymptotically guaranteed stability (2011, methodology): “This approximation has been used in model reduction and controller reduction, see [16] for an overview.”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, methodology): “For complex systems, this can be a cumbersome and expensive process [3].”
Modeling of distributed parameter systems for applications—A synthesized review from time–space separation (2010, unknown): “, [132–134]), only a few studies have been presented for DPS [135–137].”