Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as "self-optimizing". In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research.

Johannes Jäschke

Yi Cao

Vinay Kariwala

Annual Reviews in Control

控制领域现在变化非常快，出现了新的主题、流行语和技术驱动的“重大挑战”。事实上，经济发展和社会需求都越来越依赖于不同系统的集合，这些系统协同工作以提供所需的服务、舒适、健康、安全和保障。因此，对方法和技术方法的需求日益增加，这些方法允许多个独立的异构系统协同操作，以提供比单个系统更广泛的能力。这些考虑适用于许多不同的领域，包括交通、医疗保健、能源和水资源管理、智慧城市、国防、社会服务、制造供应链等。此类系统的设计需要了解计算机、软件、网络、物理、化学和生物过程以及人在循环中的联合动态。

The field of control is evolving rapidly, with new themes, buzzwords, and technology-driven challenges emerging. Economic development and societal needs increasingly rely on a collection of different systems that work together to provide the necessary services, comfort, health, safety, and security. Consequently, there is a growing demand for methods and techniques that enable multiple independent heterogeneous systems to collaborate, offering broader capabilities than a single system. These considerations apply to various fields, including transportation, healthcare, energy and water resource management, smart cities, defense, social services, and manufacturing supply chains. The design of such systems requires an understanding of the joint dynamics of computer, software, networks, physical, chemical, and biological processes, as well as the role of humans in the loop.

控制领域正在迅速发展，出现了新的主题、流行语和技术驱动的重大挑战。经济发展和社会需求越来越依赖于不同系统的集合，这些系统协同工作以提供所需的服务、舒适、健康、安全和保障。因此，对方法和技术的需求日益增加，这些方法允许多个独立的异构系统协同操作，以提供比单个系统更广泛的能力。这些考虑适用于许多不同的领域，包括交通、医疗保健、能源和水资源管理、智慧城市、国防、社会服务以及制造供应链等。此类系统的设计需要理解计算机、软件、网络、物理、化学和生物过程，以及人在循环中的联合动态。

Self-optimizing control – A survey

In this comprehensive survey on methods for finding self-optimizing controlled variables, the progress made during the last fifteen years is summarized and brute-force methods, local methods based on linearization, data and regression based methods, and methods forFinding nonlinear controlled variables for polynomial systems are presented.

Category	Quartile
计算机科学	2
计算机科学, 自动化与控制系统	2

Self-optimizing control – A survey

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Journal Info

Category	Quartile
AUTOMATION & CONTROL SYSTEMS	1