A state-space approach for the control of multivariable dynamically substructured systems

Abstract

Dynamic substructuring is an experimental technique which decomposes a complete dynamical system into a number of sub-components. Critical components are physically tested at full-size and the remaining components are simultaneously simulated in real-time. High-quality control is required to synchronize the responses of the physical and numerical components and to compensate for additional dynamics introduced by actuator systems within the physical substructures. This paper presents a new state-space approach for the analysis and synthesis of dynamically substructured systems and the associated synchronizing controller design. A new state-space substructured framework is also developed to support the synthesis of state-space dynamic models, which then leads to design and analysis of state-feedback, H∞, and adaptive controllers. This framework is applied to an experimental single-mode, quasi-motorcycle substructuring problem, for illustration of the concepts and for the comparison of controller performance. Implementation results demonstrated the improved performance resulting from the new approach and also the effectiveness of adaptive control in coping with uncertain and changing parameters within the physical substructures.

<b>Actuator Control for a Rapid Prototyping Railway Bogie, Using a Dynamically Substructured Systems Approach</b>

“…This implied that the measured force was treated as an input and the demanded actuator displacement as an output, which was the opposite of the case with the original RPB controller, being introduced for reasons of experimental convenience. Although the original transfer function DSS approach resulted in successful performance using the same ACTLab rig in previous work [5], the state-space DSS approach by the same authors [6] was adopted. It was noted that the synchronising controller of the transfer function based approach is generally of a high order and if applied to the RPB (which is more complex dynamically than the ACTLab rig), numerical conditioning could be a problem.…”

“…It was noted that the synchronising controller of the transfer function based approach is generally of a high order and if applied to the RPB (which is more complex dynamically than the ACTLab rig), numerical conditioning could be a problem. Figure 7 shows the framework of the state-space DSS approach based on reference [6]. Here, the entire emulated system is decomposed into two substructures, {Σ N , Σ P }; Σ N is the numerical substructure, Σ P is the physical one.…”

Basic examination of two substructuring schemes for shake table tests

“…Although the basic control synthesis (i.e., LSC) was mainly limited to linear substructuring systems, nonlinear substructuring control, which has been developed by applying nonlinear signal‐based control to LSC, is available for nonlinear substructuring systems. Other advanced control methods, such as model adaptive control, have also been incorporated into DSS . Although such advanced methods are available, this study primarily examines the basic performance of DSS that employs LSC.…”

“…Other advanced control methods, such as model adaptive control, have also been incorporated into DSS. 21,25,26 Although such advanced methods are available, this study primarily examines the basic performance of DSS that employs LSC.…”

A comparative study and unification of two methods for controlling dynamically substructured systems

“…Implementation tests of DSS have verified the improvements in both stability and robustness that can be achieved by this method . Moreover, the synthesis and implementation of a state–space version of DSS have been shown to be relatively straightforward exercises, resulting in the DSS synchronisation of multivariable substructuring problems and a test for ‘substructurability’, similar to the matrix rank test for controllability .…”