Hybrid Model Reference Adaptive Control of Piecewise Affine Systems

Bernardo, Mario di; Montanaro, Umberto; Santini, Stefania

doi:10.1109/tac.2012.2212516

Cited by 93 publications

(80 citation statements)

References 35 publications

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“…Therefore, they not necessarily are the best options to be applied to the examined hospital buildings, such as: whole envelope insulation, windows replacement, cool materials for mitigating the summer cooling load [38], advanced HVAC control system [39], AHU heat recovery units, renewable energy technologies; etc. Unfortunately, all such additional actions could not be considered in this analysis for feasibility reasons.…”

Section: Discussionmentioning

confidence: 99%

Dynamic energy performance analysis: Case study for energy efficiency retrofits of hospital buildings

Buonomano¹,

Calise²,

Ferruzzi³

et al. 2014

Energy

113

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Section: Discussionmentioning

confidence: 99%

Dynamic energy performance analysis: Case study for energy efficiency retrofits of hospital buildings

Buonomano¹,

Calise²,

Ferruzzi³

et al. 2014

Energy

113

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“…Following work presented in [8], [9], [15] and [16] the switching signal σ(t) is assumed to be known. As this is a strong assumption, future work is dedicated to remove it.…”

Section: Pwa Systems and Adaptive Identificationmentioning

confidence: 99%

Concurrent learning adaptive identification of piecewise affine systems

Kersting

Buss

2014

53rd IEEE Conference on Decision and Control

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In this paper, we enhance a recently proposed method for adaptive identification of piecewise affine systems by the use of concurrent learning. It is shown that the concurrent use of recorded and instantaneous data leads to exponential convergence of all subsystem parameters under verifiable conditions on the recorded data. A key advantage of the proposed method is that linear independence of the recorded data is sufficient, compared to the persistence of excitation assumed by previous adaptive parameter identifiers. Furthermore, the procedure tremendously improves the performance of adaptive identification for piecewise affine systems that previously suffered from slow convergence. 53rd IEEE Conference on Decision and Control

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“…Unfortunately, up to now, there exist only relatively few results about the passivity-based tracking control for switched systems. By using passivity, Bernardo et al 32,33 studied the model reference adaptive control problems for piecewise affine systems and discrete-time piecewise linear systems, respectively. Recently, using incremental passivity, Dong and Zhao 34 and Li and Zhao 35 have investigated the output tracking control problem and the output regulation problem for switched nonlinear systems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Passivity‐based adaptive output tracking control for switched nonlinear systems with uncertain parameters

Sun

Zhao

2017

Adaptive Control & Signal

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SummaryThis paper addresses the issue of the adaptive output tracking control for switched nonlinear systems with uncertain parameters. The solvability of the tracking control problem for each subsystem is not necessary to hold. Individual update laws corresponding to different unknown parameters are adopted to reduce the conservativeness produced from the adoption of a common undated law. By means of the dual design of the adaptive controllers and a state-dependent switching law using multiple storage functions technique, several conditions are obtained under which the adaptive output tracking control problem for switched nonlinear systems is solvable. Finally, an example shows the effectiveness of the proposed method.

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Hybrid Model Reference Adaptive Control of Piecewise Affine Systems

Cited by 93 publications

References 35 publications

Dynamic energy performance analysis: Case study for energy efficiency retrofits of hospital buildings

Dynamic energy performance analysis: Case study for energy efficiency retrofits of hospital buildings

Concurrent learning adaptive identification of piecewise affine systems

Passivity‐based adaptive output tracking control for switched nonlinear systems with uncertain parameters

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