On some continuous-time modeling and estimation problems for control and communication

The scope of the thesis is to estimate the parameters of continuous-time models used within control and communication from sampled data with high accuracy and in a computationally efficient way.In the thesis, continuous-time models of systems controlled in a networked environment, errors-in-variables systems, stochastic closed-loop systems, and wireless channels are considered. The parameters of a transfer function based model for the process in a networked control system are estimated by a covariance function based approach relying upon the second order statistical properties of input and output signals. Some other approaches for estimating the parameters of continuous-time models for processes in networked environments are also considered. The multiple input multiple output errors-in-variables problem is solved by means of a covariance matching algorithm. An analysis of a covariance matching method for single input single output errors-in-variables system identification is also presented. The parameters of continuous-time autoregressive exogenous models are estimated from closed-loop filtered data, where the controllers in the closed-loop are of proportional and proportional integral type, and where the closed-loop also contains a time-delay. A stochastic differential equation is derived for Jakes's wireless channel model, describing the dynamics of a scattered electric field with the moving receiver incorporating a Doppler shift.The thesis consists of five main parts, where the first part is an introduction- Parts II-IV are based on the following articles:Part II - Networked Control Systems1. Y. Irshad, M. Mossberg and T. Söderström. System identification in a networkedenvironment using second order statistical properties.A versionwithout all appendices is published as Y. Irshad, M. Mossberg and T. Söderström. System identification in a networked environment using second order statistical properties. Automatica, 49(2), pages 652–659, 2013.Some preliminary results are also published as M. Mossberg, Y. Irshad and T. Söderström. A covariance function based approachto networked system identification. In Proc. 2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems, pages 127–132, Annecy,France, September 13–14, 20102. Y. Irshad and M. Mossberg. Some parameters estimation methods applied tonetworked control systems.A journal submission is made. Some preliminary results are published as Y. Irshad and M. Mossberg. A comparison of estimation concepts applied to networked control systems. In Proc. 19th Int. Conf. on Systems, Signals andImage Processing, pages 120–123, Vienna, Austria, April 11–13, 2012.Part III - Errors-in-variables Identification3. Y. Irshad and M. Mossberg. Continuous-time covariance matching for MIMOEIV system identification. A journal submission is made.4. T. Söderström, Y. Irshad, M. Mossberg and W. X. Zheng. On the accuracy of acovariance matching method for continuous-time EIV identification. Provisionally accepted for publication in Automatica.Some preliminary results are published as T. Söderström, Y. Irshad, M. Mossberg, and W. X. Zheng. Accuracy analysis of a covariance matching method for continuous-time errors-in-variables system identification. In Proc. 16th IFAC Symp. System Identification, pages 1383–1388, Brussels, Belgium, July 11–13, 2012.Part IV - Wireless Channel Modeling5. Y. Irshad and M. Mossberg. Wireless channel modeling based on stochasticdifferential equations .Some results are published as M. Mossberg and Y. Irshad. A stochastic differential equation forwireless channelsbased on Jakes’s model with time-varying phases, In Proc. 13th IEEEDigitalSignal Processing Workshop, pages 602–605, Marco Island, FL, January4–7, 2009.Part V - Closed-loop Identification6. Y. Irshad and M. Mossberg. Closed-loop identification of P- and PI-controlledtime-delayed stochastic systems.Some results are published as M. Mossberg and Y. Irshad. Closed-loop identific ation of stochastic models from filtered data, In Proc. IEEE Multi-conference on Systems and Control,San Antonio, TX, September 3–5, 2008