Linear system identification is a well-known methodology for building mathematical models of dynamic systems from observed input–output data. It also represents an essential tool for model-based control design, adaptive control and other advanced control techniques. Use of linear identification is, however, often limited to academic environment and to research facilities equipped with scientific computing platforms and highly qualified staff. Common industrial or building control system technology rarely uses these advanced design techniques. The main obstacle is typically lack of experience with their practical implementation. In this article, a procedure is proposed, implemented, and tested, that brings the benefits of linear identification into broader control system practice. The open-source DCU control system platform with its advanced control framework is used for implementation of the proposed linear identification procedure. The procedure is experimentally tested in the laboratory setting using a unique model of HVAC system as well as in real-world environment in an experimental two storey family house. Testing this novel feature of the control system has proved satisfactory results, while some of them are presented in graphical and numerical form.
Characterization and quantification of dynamic measurements is an ongoing area of research in the metrological community, as new calibration methods are being developed to address dynamic measurement applications. In the development undertaken to date, one largely assumes that nominally linear transducers can be used with linear assumptions in deconvolution of the input from the response and in system identification. To quantify the errors that arise from these assumptions, in this article, the effects of weak nonlinearities in transducers that are assumed to behave linearly during dynamic excitations are studied. Specifically, a set of first-order and second-order systems, which can model many transducers with weak nonlinearities, are used to numerically quantify the systemic errors due to the linear assumptions underlying the deconvolution. We show through the presented results the evolution of different error metrics over a large parameter space of possible transducers. Additionally, an example of quantification of the errors due to linear assumptions in system identification is demonstrated by using a time-series sparse regression system identification strategy. It is shown that the errors generated from linear identification of a nonlinear transducer can counteract the systemic errors that arise in linear deconvolution when the linear system identification is performed in similar loading conditions. In general, the methodology and results presented here can be useful for understanding the effect of nonlinearity in single degree of freedom transient dynamics deconvolution and specifically in specifying certain metrics of errors in transducers with known weak nonlinearities.