Synthesis and control of generalised dynamically substructured systems

The experimental technique for testing engineering systems via the method of dynamic substructuring is receiving significant global interest, for example in the fields of large-scale structural, aerospace, and automotive system testing. Dynamically substructured systems (DSSs) enable full-size, critical components of a complete system to be physically tested in real-time, within a laboratory environment, while the remainder of the system is modelled numerically. The intention is that the combined physical-numerical DSS behaves as if it were the complete (or emulated) system.In an ideal mechanical DSS, for example, perfect synchronization of displacements and forces at the interfaces between the numerical and physical components (or substructures) is required. Hence, a key design feature of successful DSS systems is the high fidelity of the control action. Equally, a DSS controller must be able to cope with non-linear, time-varying, and uncertain parameters within the physical substructure dynamics.The main purpose of this paper is to present a generalized DSS framework, together with associated linear and adaptive control strategies, that are specifically tailored to achieve high synchronization performance. The initial studies of this problem, as described in an earlier paper by Stoten and Hyde, are therefore continued by generalizing both the DSS dynamics and the control strategies to include (a) a number of newly defined modes of operation and (b) multivariable dynamics. In addition, comparative implementation and simulation studies are included, based upon the DSS testing of a mechanical system (a planar quasi-motorcycle rig), which was specifically designed to highlight the main features of this research. The comparative studies show that excellent DSS control can be achieved, especially with the addition of an adaptive component to the controller, despite significant changes to the physical substructure dynamics

Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

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Model predictive control of dynamically substructured systems with application to a servohydraulically-actuated mechanical plant

Copyright ©2010 Institution of Engineering and Technology (IET). This paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory and Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.Dynamically substructured systems (DSS) are increasingly used by the dynamics testing community. DSS involves the physical testing of full-size critical components in parallel with numerical testing of the remaining components. This has certain advantages over other testing methods. However, the synchronisation of the signals at the interface between the physical and numerical substructures of DSS requires a high fidelity controller. In practice, the performance of the DSS testing can be degraded by input saturation of the actuators. In this study, the authors use model predictive control (MPC) to cope with the saturation problem in DSS. To facilitate the MPC and observer design for DSS, a modified DSS framework based on an existing one is proposed. As a case study, a quasi-motorcycle (QM) system is converted into the modified DSS framework and a traditional on-line MPC control strategy is implemented in real time

Gas expulsion in highly substructured embedded star clusters

We investigate the response of initially substructured, young, embedded star clusters to instantaneous gas expulsion of their natal gas. We introduce primordial substructure to the stars and the gas by simplistically modelling the star formation process so as to obtain a variety of substructure distributed within our modelled star forming regions. We show that, by measuring the virial ratio of the stars alone (disregarding the gas completely), we can estimate how much mass a star cluster will retain after gas expulsion to within 10% accuracy, no matter how complex the background structure of the gas is, and we present a simple analytical recipe describing this behaviour. We show that the evolution of the star cluster while still embedded in the natal gas, and the behavior of the gas before being expelled, are crucial processes that affect the timescale on which the cluster can evolve into a virialized spherical system. Embedded star clusters that have high levels of substructure are subvirial for longer times, enabling them to survive gas expulsion better than a virialized and spherical system. By using a more realistic treatment for the background gas than our previous studies, we find it very difficult to destroy the young clusters with instantaneous gas expulsion. We conclude that gas removal may not be the main culprit for the dissolution of young star clusters.Comment: 19 pages, 8 figures, 2 tables. Accepted for publication in MNRA

Galaxy Cluster Mass Reconstruction Project: III. The impact of dynamical substructure on cluster mass estimates

With the advent of wide-field cosmological surveys, we are approaching samples of hundreds of thousands of galaxy clusters. While such large numbers will help reduce statistical uncertainties, the control of systematics in cluster masses is crucial. Here we examine the effects of an important source of systematic uncertainty in galaxy-based cluster mass estimation techniques: the presence of significant dynamical substructure. Dynamical substructure manifests as dynamically distinct subgroups in phase-space, indicating an ‘unrelaxed’ state. This issue affects around a quarter of clusters in a generally selected sample. We employ a set of mock clusters whose masses have been measured homogeneously with commonly used galaxy-based mass estimation techniques (kinematic, richness, caustic, radial methods). We use these to study how the relation between observationally estimated and true cluster mass depends on the presence of substructure, as identified by various popular diagnostics. We find that the scatter for an ensemble of clusters does not increase dramatically for clusters with dynamical substructure. However, we find a systematic bias for all methods, such that clusters with significant substructure have higher measured masses than their relaxed counterparts. This bias depends on cluster mass: the most massive clusters are largely unaffected by the presence of significant substructure, but masses are significantly overestimated for lower mass clusters, by ∼10 per cent at 1014 and > or ~20 per cent for < or ~ 1013.5. The use of cluster samples with different levels of substructure can therefore bias certain cosmological parameters up to a level comparable to the typical uncertainties in current cosmological studies