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.

Summary Dynamically substructured system (DSS) techniques separate critical components of a complete structural system to be physically tested in full size; the remaining linear subsystems are tested numerically. Successful and robust DSS tests rely on a high‐quality controller to cope with undesired disturbances surrounding the real‐time environment and thus ensure synchronised responses of the numerical and physical substructure outputs. Three DSS control systems are compared in this paper, which use dynamics‐based ordinary differential equations or geometry‐based delay differential equations to model the systems. Even though the control designs are not new, a series of new experimental and analytical results capture the essence of DSS control problems in a simple way, showing that (i) reliable DSS tests depend on well‐defined dynamics and numerical computational accuracy in the control design, (ii) dynamics‐based methods lay a relatively transparent and systematic foundation for deeper investigation into robustness issues and (iii) an understanding of potential and fundamental real‐time difficulties is important in order to give hints for accurate modelling, control redesign and quality improvement. Copyright © 2014 John Wiley & Sons, Ltd.