Control of Nonlinear Distributed Process Systems: Recent Developments and Challenges

hile most deal with vv processes undergraduate process control courses still dynamics and control of lumped chemical using linear transfer function models, key technological needs in growth areas such as semiconductor manufacturing, nanotechnology, biotechnology, and unmanned aerial vehicles have motivated extensive research on analysis and control of complex nonlinear distributed systems across all engineering disciplines. From a control point of view, the distinguishing feature of complex distributed systems 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 systems cannot be effectively controlled with control methods which assume that the state, manipulated and tobe-controlled variables exhibit lumped behavior or with linear control algorithms derived on the basis of IinearAinearized distributed models. icant nonuniformity of the wafer temperature profile. This, in turn, may lead to film deposition uniformity that does not meet the tight requirements set by the industry (SIA, 1997). This technological need and the complex character (nonlinearities, spatial variations, batch nature) of the RTCVD process motivate the need to control the wafer temperature profile using a nonlinear feedback controller based on a distributed process model. A typical example of this class of problems is the control of titania aerosol reactors to achieve a nearly monodisperse particle-size distribution (Kalani and Christofides, 2000); this is required for titania pigments to obtain the maximum hiding powder per unit mass. Titania aerosol Control of Particle-Size Distributions.