Techniques for Control of Distributed Process Systems

Over the last decade, 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 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. Distributed control problems of particular interest to process control researchers and engineers include regulation of spatial profiles in processes with coupled transport and reaction phenomena, control of material microstructure in thin films and coatings, and shaping of size distribution in particulate products. Such distributed control problems may be encountered in processes ranging from tubular reactors, crystal growth and chemical vapor deposition to crystallization, emulsion polymerization and aerosol synthesis of nanoparticles.

The complex nature of distributed process systems does not allow for an effective solution of distributed control problems 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 parameter models. Motivated by this, research over the last decade has led to the development of advanced model reduction techniques for deriving accurate low-order approximations of various classes of nonlinear DPS, and have recently led to a systematic framework for the synthesis of nonlinear low-order feedback controllers for nonlinear DPS that arise in the modeling of transport-reaction and particulate processes. The objective of this tutorial will be to present a thorough overview of these techniques and their applications to control of distributed process systems.

1. Overview of Distributed Process Control Problems and Modeling of Distributed Process Systems (40 minutes - covered by P. D. Christofides)

Control of spatial profiles
Control of size distributions
Control of material microstructure
Control of fluid dynamic systems

2. Techniques for control of nonlinear distributed parameter systems (135 minutes, split into three 45 minute sessions - covered by P. D. Christofides, D. Dochain and P. Daoutidis)

Nonlinear parabolic and hyperbolic PDEs: Order reduction, feedback control design, closed-loop stability.

Handling of practical control issues: robust and adaptive control design for model uncertainty compensation, control subject to input and state constraints including Lyapunov-based and predictive control, control subject to delays, reduction and control of two-time-scale hyperbolic PDEs.

Representative examples will be used throughout to illustrate the techniques.

3. Applications to advanced materials processing, particulate processes and fluid dynamic systems (50 minutes - covered by P. D. Christofides and P. Daoutidis).

4. Techniques for control of stochastic distributed parameter systems (90 minutes, split into two 45 minute sessions - covered by P.D. Christofides and A. Armaou)

Modeling of stochastic processes with emphasis on thin film growth, identification of stochastic distributed models, feedback control design, applications

Throughout the day, unresolved issues both in the theoretical and the application side will be discussed after the presentation of the developed techniques.

After each session, there will be a 10 minute break.