A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

Morato, Marcelo Menezes; Jungers, Marc; Normey-Rico, Julio E.; Sename, Olivier

doi:10.1016/j.jfranklin.2022.09.011

Cited by 5 publications

(5 citation statements)

References 51 publications

(100 reference statements)

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“…This is the so called min.-max. approach 6 . Other possible solutions are to use a nominal model without the term  𝑤 𝑤(𝑘 + 𝑗|𝑘) and consider constraints tightening, e.g.…”

Section: 𝜉 𝑥mentioning

confidence: 99%

“…In order to thoroughly debate the proposed MPC formulation synthesized with the IQC arguments, we compare it to the standard approach in the literature involving Lyapunov arguments to handle the nonlinearity using the sector condition, as done in 5,6,7 . Henceforth, we refer to this approach as traditional MPC and recall that the corresponding terminal ingredients are synthesized using Corollary 1 with  =  DG (see Remark 3).…”

Section: Numerical Examplementioning

confidence: 99%

“…Regarding the control of Lur'e systems, which are addressed in this work, the usual approach in the MPC literature (e.g. 3,4,5,6,7 ) is to replace the nonlinear interconnection equation constraint by sector conditions applied over the interconnection signals, e.g. 8,9 .…”

Section: Introductionmentioning

confidence: 99%

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Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers*

Morato

Holicki

Scherer

2023

Preprint

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In this paper, we discuss how to synthesize stabilizing Model Predictive Control (MPC) algorithms based on convexly parameterized Integral Quadratic Constraints (IQCs), with the aid of general multipliers. Specifically, we consider Lur’e systems subject to sector-bounded and slope-restricted nonlinearities. As the main novelty, we introduce point-wise IQCs with storage in order to accordingly generate the MPC terminal ingredients, thus enabling closed-loop stability, strict dissipativity with regard to the nonlinear feedback, and recursive feasibility of the optimization. Specifically, we consider formulations involving both static and dynamic multipliers, and provide corresponding algorithms for the synthesis procedures. The major benefit of the proposed approach resides in the flexibility of the IQC framework, which is capable to deal with many classes of uncertainties and nonlinearities. Moreover, for the considered class of nonlinearities, our method yields larger regions of attraction of the synthesized predictive controllers (with reduced conservatism) if compared to the standard approach to deal with sector constraints from the literature.

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“…This is the so called min.-max. approach 6 . Other possible solutions are to use a nominal model without the term  𝑤 𝑤(𝑘 + 𝑗|𝑘) and consider constraints tightening, e.g.…”

Section: 𝜉 𝑥mentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

See 1 more Smart Citation

Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers*

Morato

Holicki

Scherer

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to thoroughly debate the proposed MPC formulation synthesized with the IQC arguments, we compare it to the standard approach in the literature involving Lyapunov arguments to handle the nonlinearity using the sector condition, as done in References 5‐7. Henceforth, we refer to this approach as traditional MPC and recall that the corresponding terminal ingredients are synthesized using Corollary 1 with

𝒩 = 𝒩_{DG}

(see Remark 3).…”

Section: Numerical Examplementioning

confidence: 99%

“…In this work, we study the application of MPC for regulating so-called Lur'e systems, that is, linear systems connected in feedback with memory-less static nonlinearities. While traditional approaches in the literature replace the characterizing nonlinear interconnection equation constraint by sector inequality constraints over the interconnection signals, [3][4][5][6][7][8][9][10] our proposed method is based on employing more general integral quadratic constraints (IQCs) to effectively handle these nonlinearities. Note that the traditional approaches essentially correspond to utilizing IQCs characterized by rather restrictive classes of static multipliers instead of dynamic (i.e., frequency-dependent) ones.…”

Section: Introductionmentioning

confidence: 99%

Stabilizing model predictive control synthesis using integral quadratic constraints and full‐block multipliers

Morato,

Holicki,

Scherer

2023

Intl J Robust & Nonlinear

Self Cite

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SummaryIn this paper, we discuss how to synthesize stabilizing model predictive control (MPC) algorithms based on convexly parameterized integral quadratic constraints (IQCs), with the aid of general multipliers. Specifically, we consider Lur'e systems subject to sector‐bounded and slope‐restricted nonlinearities. As the main novelty, we introduce point‐wise IQCs with storage in order to accordingly generate the MPC terminal ingredients, thus enabling closed‐loop stability, strict dissipativity with regard to the nonlinear feedback, and recursive feasibility of the optimization. Specifically, we consider formulations involving both static and dynamic multipliers, and provide corresponding algorithms for the synthesis procedures. The major benefit of the proposed approach resides in the flexibility of the IQC framework, which is capable to deal with many classes of uncertainties and nonlinearities. Moreover, for the considered class of nonlinearities, our method yields larger regions of attraction of the synthesized predictive controllers (with reduced conservatism) if compared to the standard approach to deal with sector constraints from the literature.

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A new input design framework for asymptotic active fault diagnosis with application to integrated diagnosis and control

2024

Automatica

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A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

Cited by 5 publications

References 51 publications

Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers*

Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers*

Stabilizing model predictive control synthesis using integral quadratic constraints and full‐block multipliers

A new input design framework for asymptotic active fault diagnosis with application to integrated diagnosis and control

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