Application-Oriented Input Design in System Identification: Optimal Input Design for Control [Applications of Control]

Annergren, Mariette; Larsson, Christian A.; Hjalmarsson, Håkan; Bombois, Xavier; Wahlberg, Bo

doi:10.1109/mcs.2016.2643243

Cited by 47 publications

(33 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Broadly, implicit dual control involves obtaining an approximate solution to the Bellman equation (), which has the learning component implicitly included through the hyperstate as discussed above . Explicit dual control, conversely, generally refers to replacing Problem 1 with a surrogate optimization problem that accounts for a measure of reducible model uncertainty . Thus, explicit methods incorporate some form of probing into the optimal control problem explicitly through approximation of Problem 1.…”

Section: Optimal Control With Active Learningmentioning

confidence: 99%

Model predictive control with active learning under model uncertainty: Why, when, and how

et al. 2018

View full text Add to dashboard Cite

Optimal control relies on a model, which is generally uncertain because of incomplete knowledge of the system and changes in the dynamics over time. Probing the system under closed‐loop control can reduce the model uncertainty through generating input‐output data that is more informative than the data generated from normal operation. This paper addresses the problem of model predictive control (MPC) with active learning, with a particular focus on how incorporating probing in the control action can reduce model uncertainty. We discuss some of the central theoretical questions in this problem, and demonstrate the potential of active learning for maintaining MPC performance in the presence of uncertainty in model parameters and structure. Simulation results show that active learning is particularly beneficial when a system undergoes abrupt changes (such as the sudden occurrence of a fault) that can compromise operational safety, reliability, and profitability. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3071–3081, 2018

show abstract

Section: Optimal Control With Active Learningmentioning

confidence: 99%

Model predictive control with active learning under model uncertainty: Why, when, and how

et al. 2018

View full text Add to dashboard Cite

show abstract

“…With the aforementioned definitions and constraints, the mathematical problem at hand is outlined in Figure 2. Optimal input design for linear system identification has been studied for a long time and is still an active area of research (see Mehra [1974], Annergren et al [2017]). Although not comprehensive, this paragraph would touch upon some of the seminal pieces of work in this area and discuss in brief, the methodologies used therein.…”

Section: Figurementioning

confidence: 99%

Optimal input design for system identification using spectral decomposition

Mohan

Mithun

Bhikkaji

2018

International Journal of Control

View full text Add to dashboard Cite

The aim of this paper is to design a band-limited optimal input with power constraints for identifying a linear multi-input multi-output system. It is assumed that the nominal system parameters are specified. The key idea is to use the spectral decomposition theorem and write the power spectrum as φu(jω) = 1 2 H(jω)H * (jω). The matrix H(jω) is expressed in terms of a truncated basis for L 2 ([−ωcut-off, ωcut-off]). With this parameterization, the elements of the Fisher Information Matrix and the power constraints turn out to be homogeneous quadratics in the basis coefficients. The optimality criterion used are the well-known D−optimality, A−optimality, T −optimality and E−optimality. The resulting optimization problem is non-convex in general. A lower bound on the optimum is obtained through a bi-linear formulation of the problem, while an upper bound is obtained through a convex relaxation. These bounds can be computed efficiently as the associated problems are convex. The lower bound is used as a sub-optimal solution, the sub-optimality of which is determined by the difference in the bounds. Interestingly, the bounds match in many instances and thus, the global optimum is achieved. A discussion on the non-convexity of the optimization problem is also presented. Simulations are provided for corroboration.

show abstract

“…However, these methods are often not applicable because industrial machines are often too heavy for commercially available shakers to support and excite the system. Moreover, in industry it is important to provide sufficiently accurate models on the one hand with the least-costly identification experiment on the other hand [16]. Therefore, estimation methods that allow the machine to perform under operating conditions are highly preferred.…”

Section: Introductionmentioning

confidence: 99%

“…The second contribution is a method to evaluate the spectral power and directions of the base frame excitations, and how its result can be used for input design of the shaker excitation signals. In this respect, the proposed method can be seen as optimal input design [16]. A third contribution is given by the application of the identification and input design methods to a heavy-weight active vibration isolation system.…”

Section: Introductionmentioning

confidence: 99%

Experimental estimation of transmissibility matrices for industrial multi-axis vibration isolation systems

Beijen

Voorhoeve

Heertjes

et al. 2018

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

Application-Oriented Input Design in System Identification: Optimal Input Design for Control [Applications of Control]

Cited by 47 publications

References 76 publications

Model predictive control with active learning under model uncertainty: Why, when, and how

Model predictive control with active learning under model uncertainty: Why, when, and how

Optimal input design for system identification using spectral decomposition

Experimental estimation of transmissibility matrices for industrial multi-axis vibration isolation systems

Contact Info

Product

Resources

About