Model selection, identification and robust control for dynamical systems

To fully exploit new technologies for response mitigation and structural health monitoring, improved system identification and controller design methodologies are desirable that explicitly treat all the inherent uncertainties. In this thesis, a probabilistic framework is presented for model selection, identification and robust control of smart structural systems under dynamical loads, such as those induced by wind or earthquakes. First, a probabilistic based approach is introduced for selecting the most plausible class of models for a dynamical system using its response measurements. The proposed approach allows for quantitatively comparing the plausibility of different classes of models among a specified set of classes.; Then, two probabilistic identification techniques are presented. The first one is for modal identification using nonstationary response measurements and the second one is for updating nonlinear models using incomplete noisy measurements only. These methods allow for updating of the uncertainties associated with the values of the parameters controlling the dynamic behavior of the structure by using noisy response measurements only. The probabilistic framework is very well-suited for solving this nonunique problem and the updated probabilistic description of the system can be used to design a robust controller of the system. It can also be used for structural health monitoring.; Finally, a reliability-based stochastic robust control approach is used to design the controller for an active control system. Feedback of the incomplete response at earlier time steps is used, without any state estimation. The optimal controller is chosen by minimizing the robust failure probability over a set of possible models for the system. Here, failure means excessive levels of one or more response quantities representative of the performance of the structure and the control devices. When calculating the robust failure probability, the plausibility of each model as a representation of the system's dynamic behavior is quantified by a probability distribution over the set of possible models; this distribution is initially based on engineering judgement, but it can be updated using the aforementioned system identification approaches if dynamic data become available from the structure. Examples are presented to illustrate the proposed controller design procedure, which includes the procedure of model selection, identification and robust control for smart structures.