Risk-informed control systems for improved operational performance and decision-making

Abstract

Autonomous systems, including airborne, land-based, marine, and underwater vehicles, are increasingly present in the world. One important aspect of autonomy is the capability to process information and to make independent decisions for achieving a mission goal. Information on the level of risk related to the operation may improve the decision-making process of autonomous systems. This article describes the integration of risk analysis methods with the control system of autonomous and highly automated systems that are evaluated during operation. Four main areas of implementation are identified; (i) risk models used to directly make decisions, (ii) use of the output of risk models as input to decision-making and optimization algorithms, (iii) the output of risk models may be used as a constraint in or modifying constraints of algorithms, and (iv) the output of risk models may be used to inform representations or maps of the environment to be used in path planning. A case study on a dynamic positioning controller of an offshore supply vessel exemplifies the concepts described in this article. In addition, it demonstrates how risk model output may be used within a hybrid controller.

Strategic Management Practices as Fundamental to the Achievement of Organizational Performances

“…As defined by Marodin et al (2023), operational performance refers to the strategic attributes of companies measured using an operational-level approach, such as responsiveness and adaptability. Operational performance pertains to a company's ability to reduce management costs, order processing time, and production lead time while improving the efficiency of raw material consumption and distribution capacity (Thieme et al, 2023). The operational performance of companies is crucial as it enhances the efficiency of production and the development of quality goods and services, leading to increased revenue and profit for enterprises.…”

Supervisory Risk Control with Application to Industrial Drone Inspection

“…[2] does not consider how the BBN is to be used for online risk control. Different possibilities for using the result of the risk analysis in control systems are discussed in [22]. Different approaches where BBNs are used for decision-making can be found in [23]- [26].…”

“…The last phase of the supervisory risk controller design is the development of the control algorithm itself. Different approaches for utilizing risk measures in control algorithms are presented in [22]. In this work, a cost optimization approach is chosen as it can directly use the evaluated risk in its cost function.…”

Editorial for the special issue on the International Workshop on Autonomous System Safety (IWASS)

“…Finally, it is important that safety is inherent to the design of the autonomous systems and, hence, their control systems. Thieme et al 5 describes the integration of risk analysis methods with the control systems of autonomous and highly automated systems. They identify four main areas of implementation: (i) risk models to directly make decisions, (ii) output of risk models as input to decision-making and optimization algorithms, (iii) output of risk models as a constraint in or modifying constraints of algorithms, and (iv) output of risk models to inform representations or maps of the environment to be used in path planning.…”