In recent years, there has been much interest in using the principle of dynamic substructuring as a framework for the testing of critical engineering components and systems. The most significant advantage of the method is that it can offer the opportunity to test full-size non-linear components within a laboratory environment. Such a test would be run in parallel with a real-time numerical simulation of the remaining part of the overall system to be emulated. Potentially, the most significant disadvantage of the method is the very high fidelity of control that is required, in order to achieve near-perfect synchronization of the test rig and the numerical model. This problem is further exacerbated by the presence of unknown and changing dynamic parameters, disturbances, and non-linearities in the test rig. The purpose of this paper is to lay a foundation for the linear control of dynamically sub-structured systems and, leading on from that, robust adaptive control via an extension to the adaptive minimal control synthesis (MCS) algorithm. Comparative simulation results from using the linear and adaptive control strategies are also included in the paper.

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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.

A solution is proposed to the estimation of upper-limb orientation using miniature accelerometers and gyroscopes. This type of measurement device has many different possible applications, ranging from clinical use with patients presenting a number of conditions such as upper motor neuron syndrome and pathologies that give rise to loss of dexterity, to competitive sports training and virtual reality. Here we focus on a design that minimizes the number of sensors whilst delivering estimates of known accuracy over a defined frequency range. Minimizing the sensor count can make the measurement system less obtrusive, as well as minimising cost and reducing the required bandwidth if using a wireless solution. Accurate measurement of movement amplitude up to 15 Hz is required in our immediate application, namely to quantify tremor in multiple sclerosis patients. The drive for low numbers of sensors and good accuracy at higher frequencies leads to a novel design based on composite filters. The simple estimator structure also gives good insight into the fundamental accuracy limitations based on the sensors chosen. This paper defines the underlying mathematics, and quantifies performance for an estimator for shoulder, upper arm, lower arm and hand orientations. Good estimation accuracy up to 15 Hz is indicated, and this with a reduced total sensor count of 18 compared to 24 that would be required for more conventional estimator architectures.