Fixed-structure LPV-IO controllers: An implicit representation based approach

Wollnack, Simon; Abbas, Hossam S.; Tóth, Roland; Werner, Herbert

doi:10.1016/j.automatica.2017.06.009

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…Rewriting the LPV system representation in this way is commonly utilized in LPV I/O identification, e.g., Laurain et al (2010), and LPV subspace identification, e.g., Van Wingerden and Verhaegen (2009); Cox and Tóth (2021). Furthermore, note that this augmentation does not influence the order of the system (Wollnack et al, 2017). Note 1.…”

Section: Alternative Representation Formmentioning

confidence: 95%

Data-Driven Predictive Control for Linear Parameter-Varying Systems

Verhoek,

Abbas,

Tóth

et al. 2021

Preprint

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Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (I/O) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured I/O and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the I/O data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the LPV-DPC scheme, purely based on data, achieves similar performance as LPV model-based predictive control.

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Section: Alternative Representation Formmentioning

confidence: 95%

Data-Driven Predictive Control for Linear Parameter-Varying Systems

Verhoek,

Abbas,

Tóth

et al. 2021

Preprint

Self Cite

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“…Remark 4. The stability analysis for I/O LPV models can be based on implicit image representation and through Finsler's Lemma, as done in Wollnack et al, 2017) 2.3.2. State-Space + Estimation Error Constraint LPV MPC algorithms concerning the I/O formulation are very novel and a promising investigation field.…”

Section: Input/output Formulationmentioning

confidence: 99%

Model predictive control design for linear parameter varying systems: A survey

Morato

Normey‐Rico

Sename

2020

Annual Reviews in Control

130

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Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods.

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LPV modeling of nonlinear systems: A multi‐path feedback linearization approach

Abbas

Tóth

Petreczky

et al. 2021

Intl J Robust & Nonlinear

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This article introduces a systematic approach to synthesize linear parameter‐varying (LPV) representations of nonlinear (NL) systems which are described by input affine state‐space (SS) representations. The conversion approach results in LPV‐SS representations in the observable canonical form. Based on the relative degree concept, first the SS description of a given NL representation is transformed to a normal form. In the SISO case, all nonlinearities of the original system are embedded into one NL function, which is factorized, based on a proposed algorithm, to construct an LPV representation of the original NL system. The overall procedure yields an LPV model in which the scheduling variable depends on the inputs and outputs of the system and their derivatives, achieving a practically applicable transformation of the model in case of low order derivatives. In addition, if the states of the NL model can be measured or estimated, then a modified procedure is proposed to provide LPV models scheduled by these states. Examples are included to demonstrate both approaches.

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Fixed-structure LPV-IO controllers: An implicit representation based approach

Cited by 11 publications

References 11 publications

Data-Driven Predictive Control for Linear Parameter-Varying Systems

Data-Driven Predictive Control for Linear Parameter-Varying Systems

Model predictive control design for linear parameter varying systems: A survey

LPV modeling of nonlinear systems: A multi‐path feedback linearization approach

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