9612558 Dahleh We propose to carry out basic research in the areas of identification robust control, and nonlinear robust control. In the identification area, we intend to examine new paradigms in which model uncertainty is part of the model structure parametrization, such that the resulting identified models are readily usable for robust control purposes. Fundamental issues relating to identifiability of model sets, input design, algorithms, and sample complexity will be addressed. In the robust control area we propose to develop robust analysis and synthesis tools for more general classes of performance, which are more relevant to practical design objectives than the worst- case paradigms employed at present. Moreover, we will further investigate the optimal l1 problem in state space to derive a separation structure for the optimal output feedback controller. In the nonlinear area, our purpose is to provide computational analysis and design tools for classes of nonlinear systems that capture a wide range of applications. One such class is the set of linear parameter-varying systems. The goal is to provide a catalog of special problem structures and corresponding tools for analysis and controller design. In addition, we will search for structures that have low dimensional nonlinear components in such a way that the computational complexity of the analysis problem is directly a function of these nonlinear components. Other related topics will be considered. ***
