Incremental passivity and output regulation

Pavlov, Alexey; Marconi, Lorenzo

doi:10.1016/j.sysconle.2007.10.008

Cited by 158 publications

(117 citation statements)

References 13 publications

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“…The notion of u-average passivity fromū is strictly reminiscent of the concept of incremental passivity [41]. It defines incremental-like passivity of the overall system with respect to trajectories that are parametrized by different inputs u rather than time.…”

Section: B Sampled-data Average-passivitymentioning

confidence: 99%

Feedforwarding Under Sampling

Mattioni

Monaco

Normand‐Cyrot

2019

IEEE Trans. Automat. Contr.

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The paper deals with stabilization of feedforward multiple cascade dynamics under sampling. It is shown that u-average passivity concepts and Lyapunov methods can be profitably exploited to provide a systematic sampled-data design procedure. The proposed methodology recalls the continuous-time feedforwarding steps and can be applied under the same assumptions as those set over the continuous-time cascade dynamics. The final sampled feedback is carried out through a three steps procedure that involves iterative passivation and stabilization in the u-average sense. Constructive aspects are developed to compute approximate solutions which are indeed implemented in practice. An example is worked out upon the paper with comparative simulations with respect to usual sampled-and-hold implementations.

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Section: B Sampled-data Average-passivitymentioning

confidence: 99%

Feedforwarding Under Sampling

Mattioni

Monaco

Normand‐Cyrot

2019

IEEE Trans. Automat. Contr.

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“…Remark 2.2 u-average passivity fromū is strictly reminiscent of the notion of incremental passivity (see Pavlov and Marconi (2008)). It defines incremental-like passivity of the overall system with respect to trajectories that are parametrized by different inputs u rather than time.…”

Section: U-average Passivitymentioning

confidence: 99%

Forwarding stabilization in discrete time

2019

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“…Definition Let

G MathClass-rel\in {double-struckR}^{l}

. Function

f MathClass-punc: {double-struckR}^{l} MathClass-rel\to {double-struckR}^{1}

is called G‐monotonically decreasing if inequality ( x − y ) T G ( f ( x ) − f ( y )) ≤ 0 holds for all

x MathClass-punc, y MathClass-rel\in {double-struckR}^{l}

. Remark Apparently, for l = 1, G = 1 G ‐monotonic decrease of the function f is equivalent to incremental passivity of the static system with characteristics ( − f ). Definition is readily extended to dynamical systems with the state vector

x MathClass-rel\in {double-struckR}^{n}

, input

u MathClass-rel\in {double-struckR}^{m}

, and output

y MathClass-rel\in {double-struckR}^{l}

.…”

Section: Resultsmentioning

confidence: 99%

Decentralized adaptive controller for synchronization of nonlinear dynamical heterogeneous networks

Fradkov

Junussov

2012

Adaptive Control & Signal

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For a network of interconnected nonlinear dynamical systems, an adaptive leader-follower output feedback synchronization problem is considered. The proposed structure of decentralized controller and adaptation algorithm is based on speed gradient and passivity. Sufficient conditions of synchronization for one class of heterogeneous networks are established. An example of synchronization of the network of nonidentical Chua systems is analyzed. The main contribution of the paper is adaptive controller design and analysis under conditions of incomplete measurements, incomplete control, and uncertainty.

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Incremental passivity and output regulation

Cited by 158 publications

References 13 publications

Feedforwarding Under Sampling

Feedforwarding Under Sampling

Forwarding stabilization in discrete time

Decentralized adaptive controller for synchronization of nonlinear dynamical heterogeneous networks

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