Graph-Based Orchestration of Service-Oriented Model-Based Control Systems

TL;DR

By using the graph-based method for architecture adaptation, the control system is more flexible, has lower computation time, and higher accuracy than traditional configuration methods.

Abstract

This paper presents a novel graph-based method for adapting control system architectures at runtime. We use a service-oriented architecture as a basis for its formulation. In our method, adaptation is achieved by selecting the most suitable elements, such as filters and controllers, for a control system architecture to improve control systems objective based on a predefined cost function. Traditional configuration methods, such as state machines, lack flexibility and depend on a predefined control system architecture during runtime. Our graph-based method allows for dynamic changes in the control system architecture, as well as a change in its objective depending on the given system state. Our approach uses a weighted, directed graph to model the control system elements and their interaction. In a case-study with a three-tank system, we show that by using our graph-based method for architecture adaptation, the control system is more flexible, has lower computation time, and higher accuracy than traditional configuration methods.

Figures (3)

• Figure 1: Control system respresentation as graph of services.
• Figure 2: Example of control system architecture graph with start and target nodes, and instantiated service types. Edges show varying model complexities.
• Figure 3: Control system architecture graph and the shortest path based on the values from Table \ref{['tab:first']}, the cost function from Eq. \ref{['eq:cost_function']} with $\alpha_\text{comp}=1$, $\beta_\text{comp}=100$ for scenarios 1, 2 and $\alpha_\text{comp}=1000$, $\beta_\text{comp}=20$ for scenario 3 using Dijkstra's algorithm where green nodes are common for all evaluation scenarios, blue for the first scenario, red for the second, yellow for the third scenario, and white for unselected services.

Theorems & Definitions (5)

• Definition 1: Control System
• Definition 2: Control System Objective
• Definition 3: Optimal Control System Architecture
• Definition 4: Model Complexity
• Remark : Accuracy vs Inaccuracy