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Open Access Publications from the University of California

Economic and Distributed Model Predictive Control of Nonlinear Systems

  • Author(s): Heidarinejad, Mohsen
  • Advisor(s): Christofides, Panagiotis D
  • et al.
Abstract

Maximizing profit has been and will always be the primary purpose of optimal process operation. Within process control, the economic optimization considerations of a plant are usually addressed via a two-layer architecture. In general, this architecture includes: the upper layer that optimizes process operation set-points taking into account economic considerations using steady-state system models, and the lower layer (i.e., process control layer) whose primary objective is to employ feedback control systems to force the process to track the set-points. Optimizing closed-loop performance with respect to general economic considerations for nonlinear systems in a unified framework has recently become a subject of increasing theoretical interest and practical importance. In addition to a tighter integration of economics and control, advances in communication technologies have motivated augmentation of traditional point-to-point and wired local control systems with additional cheap and easy-to-install networked sensors and actuators and control systems. Networked distributed

control systems can substantially improve the efficiency, flexibility, robustness and fault tolerance of an industrial control system while reducing the installation, reconfiguration and maintenance expenses at the cost of coordination and design/redesign of different control systems in the new architecture.

This dissertation presents rigorous, yet practical, methods for the design of economic and distributed predictive control systems. Beginning with a review of recent results on the subject, the dissertation presents the design of Lyapunov-based economic model predictive control scheme for a broad class of nonlinear systems using state and output feedback. Then, the dissertation focuses on the development of an economic model predictive control method with guaranteed improvement in closed-loop performance compared to conventional Lyapunov-based model predictive control designs. Subsequently, the dissertation focuses on the design of a networked distributed model predictive control method for multirate uncertain systems subject to communication disruptions and measurement noise and distributed model predictive control method for switched systems 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 their effectiveness and performance are evaluated through extensive computer simulations.

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