Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications.

摘要

Traditionally, process control systems rely on centralized control architectures utilizing dedicated, wired links to measurement sensors and control actuators to regulate appropriate process variables at desired values. While this paradigm to process control has been successful, we arc currently witnessing an augmentation of the existing, dedicated control systems, with additional networked (wired and/or wireless) actuator/sensor devices which have become cheap and easy-to-install. While such an augmentation in sensor information, actuation capability and network-based availability of data has the potential to dramatically improve control system performance, it poses a number of new challenges in control system design that cannot be addressed with traditional control methods.;This dissertation presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems. Beginning with a review of recent results on the subject, the dissertation presents the design of model predictive control systems via Lyapunov-based control techniques, accounting for the influence of asynchronous and delayed measurements. Then, the dissertation focuses on the development of a networked control architecture, which naturally augments dedicated control systems with networked control systems and takes advantage of additional, potentially asynchronous and delayed measurements, to maintain closed-loop stability and significantly improve closed-loop performance. Subsequently, the dissertation focuses on the design of distributed predictive control systems, that utilize a fraction of the time required by the respective centralized control systems and cooperate in an efficient fashion, to compute optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications. The control methods are applied to nonlinear chemical process networks and wind-solar energy generation systems and their effectiveness and performance are evaluated through detailed computer simulations.