An EM-based identification algorithm for a class of hybrid systems with application to power electronics

In this paper we present an identification algorithm for a class of continuous-time hybrid systems. In such systems, both continuous-time and discrete-time dynamics are involved. We apply the expectation-maximisation algorithm to obtain the maximum likelihood estimate of the parameters of a discrete-time model expressed in incremental form. The main advantage of this approach is that the continuous-time parameters can directly be recovered. The technique is particularly well suited to fast-sampling rates. As an application, we focus on a standard identification problem in power electronics. In this field, our proposed algorithm is of importance since accurate modelling of power converters is required in high- performance applications and for fault diagnosis. As an illustrative example, and to verify the performance of our proposed algorithm, we apply our results to a flying capacitor multicell converter. © 2014 © 2014 Taylor & Francis

Nonlinear Sampled-Data Systems

International audienceSampled-data systems are control systems in which the feedback law is digitally implemented via a computer. They are prevalent nowadays due to the numerous advantages they offer compared to analog control. Nonlinear sampled-data systems arise in this context when either the plant model or the controller is nonlinear. While their linear counterpart is now a mature area, nonlinear sampled-data systems are much harder to deal with and, hence, much less understood. Their inherent complexity leads to a variety of methods for their modeling, analysis, and design. A summary of these methods is presented in this entry