| Key technological needs in growth areas such as semiconductor manufacturing, nanotechnology and biotechnology have motivated extensive research on the analysis and control of complex nonlinear distributed processes. Examples include temperature profile control in the Czochralski crystallization of high-purity crystals, deposition uniformity control in the chemical vapor deposition of thin films, as well as control of size distribution in the crystallization of proteins and the aerosol-based production of nanoparticles. From a control point of view, the distinguishing feature of complex distributed processes is that they give rise to nonlinear control problems that involve the regulation of highly distributed control variables by using spatially-distributed control actuators and measurement sensors. Thus, complex distributed processes cannot be effectively controlled with control methods which assume that the state, manipulated and to-be-controlled variables exhibit lumped behavior or with linear control algorithms derived on the basis of linear/linearized distributed models.; Motivated by these considerations, over the last decade, research has led to the development of a general framework for the synthesis of practically implementable nonlinear feedback controllers for complex distributed processes based on fundamental models that accurately predict their behavior. However, the developed control methods do not address two important issues: (a) the availability of actuation and sensing technologies which make possible the use of large numbers of actuators and sensors to control spatially distributed processes, and (b) the direct incorporation of state variables and manipulated input constraints in the controller design.; Motivated by the possibility of using finely spatially distributed actuation/sensing, the first part of this thesis addresses the extension of the traditional control formulation for spatially-distributed processes to an 'infinite sensing' - 'infinite actuation' formulation. Under the assumption that the target complex spatio-temporal behavior is described by a "target nonlinear partial differential equation (PDE)", combination of Galerkin's method and nonlinear control techniques is used to design nonlinear state and static output feedback controllers that enforce the desired behavior in the closed-loop system. In the second part of the thesis, we focus on the development and application of predictive-based strategies for control of PDE systems, modeling transport-reaction processes, with state variable and manipulated input constraints. Both parabolic and hyperbolic PDE systems are considered. Throughout the thesis, we use numerous examples to illustrate the application and demonstrate the advantages of the new control methods. |
