Comprehensive study of decomposition effects on distributed output tracking of an integrated process over a wide operating range

An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach.State variables in bold cannot be estimated using the available measurements of the corresponding subsystem. "NA" means that no variable is assigned to the corresponding subsystem.

Section: Proposed Subsystem Decomposition Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subsystem decomposition of process networks for simultaneous distributed state estimation and control

Yin

Liu

2018

“…The argument behind this measure is that modular organizations that arise in natural systems are nonrandom. This measure is intuitive and has seen many interesting applications; for instance, this measure has been shown to provide a flexible and powerful tool for the analysis and design of control architectures and for the decomposition of large‐scale optimization problems . A powerful generalization of Newman's measure has been proposed in Reference and here it was shown that systems of high modularity are extremum points of a Hamiltonian function.…”

Section: Introductionmentioning

confidence: 98%

Modularity measures: Concepts, computation, and applications to manufacturing systems

Shao

Zavala

2020

We propose a measure to quantify the modularity of industrial production (manufacturing) systems and optimization formulations to compute it. From a manufacturing perspective, we argue that a system is deemed modular if: (a) the equipment units that comprise it form clusters (modules) of dense connectivity (i.e., difficult module assembly tasks are performed off-site), (b) connectivity between modules is sparse (i.e., easy assembly tasks are performed on-site), (c) the number of modules is small, and (d) the module dimensions facilitate transportation. We show that the measure proposed satisfies these requirements and that it can be computed by solving a convex mixed-integer quadratic program. We provide a discussion on advantages and disadvantages of alternative modularity measures used in different scientific and engineering communities. Our results seek to highlight conceptual and computational challenges that arise from the need to define and quantify modularity in a manufacturing context.

“…Furthermore, feasible mitigation practices using control strategies after the occurrence of attacks have not yet been explored. In light of these gaps, the contributions of this work are as follows: (a) construction of data‐based machine‐learning detection algorithms which can effectively detect multiple classes of intelligent cyber‐attacks; (b) design of a robust control architecture to promptly contain and eliminate the impact of cyber‐attacks by reconfiguring the control system; and (c) application of the proposed detection and mitigation schemes to a benchmark multivariable nonlinear process example, which is a process example widely used in literature to test the performance of new control system designs . The remainder of this paper is organized as follows: notation and the class of nonlinear process systems considered are presented in Section 2; the cyber‐secure control architecture is formulated in Section 3; the design and detection mechanism of cyber‐attacks are presented in Section 4; and the application of the proposed methodology to a nonlinear chemical process network is presented in Section 5.…”

Section: Introductionmentioning

confidence: 99%

A cyber‐secure control‐detector architecture for nonlinear processes

Chen

Christofides

2020

This work presents a detector‐integrated two‐tier control architecture capable of identifying the presence of various types of cyber‐attacks, and ensuring closed‐loop system stability upon detection of the cyber‐attacks. Working with a general class of nonlinear systems, an upper‐tier Lyapunov‐based Model Predictive Controller (LMPC), using networked sensor measurements to improve closed‐loop performance, is coupled with lower‐tier cyber‐secure explicit feedback controllers to drive a nonlinear multivariable process to its steady state. Although the networked sensor measurements may be vulnerable to cyber‐attacks, the two‐tier control architecture ensures that the process will stay immune to destabilizing malicious cyber‐attacks. Data‐based attack detectors are developed using sensor measurements via machine‐learning methods, namely artificial neural networks (ANN), under nominal and noisy operating conditions, and applied online to a simulated reactor‐reactor‐separator process. Simulation results demonstrate the effectiveness of these detection algorithms in detecting and distinguishing between multiple classes of intelligent cyber‐attacks. Upon successful detection of cyber‐attacks, the two‐tier control architecture allows convenient reconfiguration of the control system to stabilize the process to its operating steady state.