Development of numerical-substructure-based and output-based substructuring controllers

Tu, Jiahuang

doi:10.1002/stc.1505

Cited by 18 publications

(60 citation statements)

References 35 publications

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“…The initial gains of ρ(0) = σ (0) = 0 were assigned, and other control parameters {N, n, α, β, γ } are summarized in table 2. Additionally, the linear numerical-substructure-based state-space controller (N-SSLSC) in [6] was used as a dynamicsbased ODE design example in the experiments for the purpose of literature comparison. N-SSLSC synthesis requires prior possession of only the nominal ODE models of {Σ N1 , G TS }, independent of the Σ 2 parameters; detail was obviated for the sake of brevity and can be seen in [6].…”

Section: (C) Implementation Studiesmentioning

confidence: 99%

“…Additionally, the linear numerical-substructure-based state-space controller (N-SSLSC) in [6] was used as a dynamicsbased ODE design example in the experiments for the purpose of literature comparison. N-SSLSC synthesis requires prior possession of only the nominal ODE models of {Σ N1 , G TS }, independent of the Σ 2 parameters; detail was obviated for the sake of brevity and can be seen in [6]. Figure 7 presents the AFP and N-SSLSC control performances in the left-and right-hand columns, respectively.…”

Section: (C) Implementation Studiesmentioning

confidence: 99%

“…DSS framework and example from the literature: (a) the substructured framework for the nominal case [6,7] and (b) the emulated MSDP system [8,9]. physical process, are applied to investigate the dynamic behaviour of a wide range of structural systems (e.g.…”

mentioning

confidence: 99%

“…[1][2][3][4][5]). In order to help present the DSS concepts and synthesize the associated dynamics in a systematic manner, a nominal substructured framework is displayed in figure 1a [6,7], in which the original, entire engineering system to be tested is called an emulated system. The emulated system is broken down into at least two heterogeneous subsystems.…”

mentioning

confidence: 99%

“…In §3, new direct-compensation and singular value decomposition (SVD) methods are introduced to improve AFP performance [7]. In order to demonstrate the concepts involved, implementation studies of two DSSs are presented in §3c as examples, and the AFP controller is compared with a linear dynamics-based controller in [6]. Based on the results derived from §2 and §3, the AFP numerical problems, least-squares methods and G TS modelling policies related to the efficacy of DSS tests are discussed in §4.…”

mentioning

confidence: 99%

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Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example

Hsiao

Chen

2014

Proc. R. Soc. A.

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Testing techniques of dynamically substructured systems dissects an entire engineering system into parts. Components can be tested via numerical simulation or physical experiments and run synchronously. Additional actuator systems, which interface numerical and physical parts, are required within the physical substructure. A high-quality controller, which is designed to cancel unwanted dynamics introduced by the actuators, is important in order to synchronize the numerical and physical outputs and ensure successful tests. An adaptive forward prediction (AFP) algorithm based on delay compensation concepts has been proposed to deal with substructuring control issues. Although the settling performance and numerical conditions of the AFP controller are improved using new directcompensation and singular value decomposition methods, the experimental results show that a linear dynamics-based controller still outperforms the AFP controller. Based on experimental observations, the least-squares fitting technique, effectiveness of the AFP compensation and differences between delay and ordinary differential equations are discussed herein, in order to reflect the fundamental issues of actuator modelling in relevant literature and, more specifically, to show that the actuator and numerical substructure are heterogeneous dynamic components and should not be collectively modelled as a homogeneous delay differential equation.

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Section: (C) Implementation Studiesmentioning

confidence: 99%

Section: (C) Implementation Studiesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example

Hsiao

Chen

2014

Proc. R. Soc. A.

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Substructurability and exact synchronisation analysis

Jiang

2013

Struct. Control Health Monit.

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SUMMARY A numerical‐substructure‐based framework for the synthesis of substructured dynamics, substructurability, and exact synchronisation controllers using state‐space methods is presented in this paper. Substructurability is used to analyse the dynamic properties of numerical substructures and transfer systems, on the basis of controllability, substructured eigenvalue, and condition number techniques. Exact synchronisation controllers are designed under the assumptions of unknown parameters of the tested components and are used to achieve a higher level of synchronisation over the transient state. Implementation studies present a synchronised‐component‐based approach for the controller design and also verify that (1) substructured eigenvalues provide a general indicator to predict stability and performance in advance and (2) slow substructured eigenvalues due to low damping or large mass within a numerical substructure tend to yield ineffective and inefficient tests. Copyright © 2013 John Wiley & Sons, Ltd.

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Dynamically substructured system frameworks with strict separation of numerical and physical components

2014

Struct. Control Health Monit.

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A dynamically substructured system (DSS) consists of both physical and numerical components. It is used for the testings of the dynamics of some systems arising from engineering problems to overcome the drawbacks of conventional testing methods. One of the key issues influencing the DSS testing accuracy is from the synchronization of the physical and numerical components. This synchronization can be achieved by a controller (called DSS controller). To facilitate the DSS controller design, this paper develops a sophisticated DSS framework with its variations to further enhance the analysis and design of DSS. The main feature of the proposed DSS framework is that it has a strict separation of numerical and physical substructures, which can enable one to explicitly identify the relations of the substructures and signals within a DSS and thus greatly facilitate more elegant treatments of DSS problems, such as DSS establishment, conversion of different DSSs, as well as uncertainties and measurement noise incorporation in robust control.The proposed framework and its variations unify many DSS problems.

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Development of numerical-substructure-based and output-based substructuring controllers

Cited by 18 publications

References 35 publications

Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example

Modelling and control issues of dynamically substructured systems: adaptive forward prediction taken as an example

Substructurability and exact synchronisation analysis

Dynamically substructured system frameworks with strict separation of numerical and physical components

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