Last updated Jun 5, 2023 Edit Source

Discusses the future of control systems in an increasingly information-rich world. The authors argue that the increasing availability of information will lead to new opportunities for control systems, but also new challenges. They identify several key areas where research is needed to address these challenges, including:

• Modeling and identification: As systems become more complex, it will become increasingly difficult to develop accurate models of their behavior. This will make it difficult to design effective control systems.
• Learning and adaptation: In an information-rich world, control systems will need to be able to learn and adapt to changes in their environment. This will require new approaches to control system design that are based on machine learning and artificial intelligence.
• Security and safety: As control systems become more interconnected, they will become more vulnerable to cyberattacks. This will require new approaches to security and safety that are designed for the information-rich world.

The authors conclude by arguing that the future of control systems is bright, but that there are significant challenges that need to be addressed. They call for a concerted effort by the control community to address these challenges and to ensure that control systems continue to play a vital role in our increasingly information-rich world.

Here are some additional details about the challenges and opportunities that the authors discuss:

• Modeling and identification: The increasing complexity of systems makes it difficult to develop accurate models of their behavior. This is a challenge for control system design because accurate models are needed to design effective controllers. The authors discuss several approaches that can be used to address this challenge, including:

  • Using data-driven methods to develop models from measured data.
  • Using physics-based models to develop models of the underlying physical processes.
  • Using hybrid models that combine data-driven and physics-based models.

• Learning and adaptation: In an information-rich world, control systems will need to be able to learn and adapt to changes in their environment. This is a challenge because traditional control systems are designed to operate in a fixed environment. The authors discuss several approaches that can be used to address this challenge, including:

  • Using machine learning to develop controllers that can learn from experience.
  • Using artificial intelligence to develop controllers that can reason about their environment and make decisions based on their knowledge.

• Security and safety: As control systems become more interconnected, they will become more vulnerable to cyberattacks. This is a challenge because cyberattacks can have serious consequences, such as causing physical damage or financial loss. The authors discuss several approaches that can be used to address this challenge, including:

  • Using security measures to protect control systems from cyberattacks.
  • Designing control systems that are resilient to cyberattacks.