Distributed identification and control of spatially interconnected systems with application to an actuated beam

Liu, Qin; Werner, Herbert

doi:10.1016/j.conengprac.2016.05.002

Cited by 13 publications

(3 citation statements)

References 29 publications

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“…Due to the lack of space, interested readers are referred to [17] for a detailed description of applying the piezoelectric FE approach together with the experimental modal analysis [18] to model the experimental test bed. The physical modelling implemented in Section III considers the flexible structure as a lumped MIMO system, and it results in a state space model of order 196.…”

Section: Fe Modelling Of a Piezo-actuated Beammentioning

confidence: 99%

Distributed identification of spatially-distributed systems based on finite element modelling

Liu

Gross

Werner

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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In structural engineering, the application of the finite element (FE) method to model a spatially-distributed system exploits physical insight into the plant dynamics and often results in a lumped model of large order, which is undesirable or even impossible for model-based controller design. This paper bridges the gap between FE modelling and distributed identification by proposing a novel identification technique to extract a distributed model from an FE model for both parameter-invariant and parameter-varying systems. The obtained models define the system dynamics on subsystems and capture the interacted behaviour among neighbouring subsystems. The proposed approach is tested on an experimental beam structure equipped with an array of piezo actuators and sensors, and the model accuracy is compared to that of blackbox identification.

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Section: Fe Modelling Of a Piezo-actuated Beammentioning

confidence: 99%

Distributed identification of spatially-distributed systems based on finite element modelling

Liu

Gross

Werner

2015

2015 54th IEEE Conference on Decision and Control (CDC)

Self Cite

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“…Spatially interconnected systems (SISs), also referred to as spatially distributed systems, are generally regarded as large-scale interconnected systems consisting of multiple spatially distributed similar subunits that only interact with their neighbors. Many practical applications, such as heat equation [3], vehicle platoons [18], and flexible structures [20], can be captured by such interconnected systems. There are also many other excellent state-space representations in the literature can be used to describe the dynamic of these systems such as Roesser model [1,22], Fornasini-Marchesini model [14,26], and multidimensional (MD) model [23,25], etc.…”

Section: Introductionmentioning

confidence: 99%

Positive Trigonometric Polynomials on the Stability of Spatially Interconnected Systems

Zhai¹

2022

Preprint

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This paper is devoted to the stability analysis of spatially interconnected systems (SISs) via the sum-of-squares (SOS) decomposition of positive trigonometric polynomials. For each spatial direction of SISs, three types of interconnected structures are considered. Inspired by the idea of rational parameterization and robust stabilizability function, necessary and sufficient conditions are derived for establishing the stability of SISs with two different combined topologies respectively. For these results, the primary issue concerns the global or local positivity of trigonometric polynomials. SOS decomposition and generalized trace parameterization of positive trigonometric polynomials are utilized so that the addressed problems can be quantified by two semidefinite programs (SDPs). The proposed methods are applicable to all possible interconnected structures due to the assumption of spatial reversibility. Numerical examples are given to illustrate the efficiency of the derived theoretical results.

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“…The system analysis, synthesis, and the distributed controllers design and implementation of spatially interconnected systems are solved in [1, 2]. There are also many other results on spatially interconnected systems with uncertainties [10–13], systems interconnected over an arbitrary graph [14], spatially interconnected systems with random parameters [15], spatially interconnected systems with delays [12, 16] and other areas [17–22].…”

Section: Introductionmentioning

confidence: 99%

Distributed finite‐time control for spatially interconnected systems

Feng

2019

IET Control Theory & Applications

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Distributed identification and control of spatially interconnected systems with application to an actuated beam

Cited by 13 publications

References 29 publications

Distributed identification of spatially-distributed systems based on finite element modelling

Distributed identification of spatially-distributed systems based on finite element modelling

Positive Trigonometric Polynomials on the Stability of Spatially Interconnected Systems

Distributed finite‐time control for spatially interconnected systems

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