Feedback identification of conductance-based models

Burghi, Thiago B.; Schoukens, Maarten; Sepulchre, Rodolphe

doi:10.1016/j.automatica.2020.109297

Search citation statements

Order By: Relevance

Paper Sections

Select...

Discussion1

⌟ VII Memristive Circuits1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article4

Other3

Relationship

Self Cite2

Independent5

Authors

Journals

Cited by 8 publications

(2 citation statements)

References 36 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results in Section 4 suggest that Model Reference Adaptive Control, ous study of how noise impacts the system identification of conductance-based models, see [31]. Robustness of the adaptive control laws against modelling uncertainty and measurement noise will be the scope of future investigation, both analytical and experimental.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Conductance Control

Raphael¹,

Burghi²,

Sepulchre³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Adaptive Conductance Control

Raphael¹,

Burghi²,

Sepulchre³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

“…In this example, we consider the potassium conductance i = G K (v), which is given by the equations Following Hodgkin and Huxley [78], the constants ḡK and v K are set to 19 m mho cm 2 and 12 mV, respectively. The dynamics in n are contractive [79,Prop. 1], therefore the potassium conductance defines an operator on L 2,T .…”

Section: ⌟ VII Memristive Circuitsmentioning

confidence: 99%

Monotone One-Port Circuits

Chaffey

Sepulchre

2024

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

Maximal monotonicity is explored as a generalization of the linear theory of passivity, aiming at an algorithmic input/output analysis of physical models. The theory is developed for maximal monotone one-port circuits, formed by the series and parallel interconnection of basic elements. These circuits generalize passive LTI transfer functions. Periodic input signals are shown to be mapped to periodic output signals, and these input-output behaviors can be efficiently computed using a maximal monotone splitting algorithm, which decomposes the computation according to the circuit topology. A new splitting algorithm is presented, which applies to any monotone circuit defined as a port interconnection of monotone elements.The research leading to these results has received funding from the European Research Council under the Advanced ERC Grant Agreement Switchlet n. 670645.

show abstract