MANAGING CLOSED CONTROL LOOPS

Organization Name

telefonaktiebolaget lm ericsson (publ)

Inventor(s)

Amin Azari of Järfälla (SE)

Swarup Kumar Mohalik of Bangalore (IN)

Pedro Henrique Gomes Da Silva of São Paulo (BR)

András Zahemszky of Sollentuna (SE)

Refik Fatih Ustok of Istanbul (TR)

Ahmet Cihat Baktir of Istanbul (TR)

Elham Dehghan Biyar of Istanbul (TR)

MANAGING CLOSED CONTROL LOOPS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240187354 titled 'MANAGING CLOSED CONTROL LOOPS

Simplified Explanation

The abstract describes a computer-implemented method for managing a plurality of closed control loops (CLS) in a control system using reinforcement learning (RL) agents to determine actions that move the environment towards a predefined target.

The method involves using RL agents to make decisions in a control system with multiple closed control loops.
The RL agent is rewarded based on how well the environment moves towards a predefined target as a result of the action taken.
This innovation combines RL with control systems to optimize performance and achieve desired outcomes.

Potential Applications

This technology could be applied in various industries such as manufacturing, robotics, and autonomous systems where complex control systems are used.

Problems Solved

This technology helps in optimizing the performance of control systems by using RL agents to make decisions, leading to more efficient and effective control loop management.

Benefits

The use of RL agents in control systems can lead to improved performance, increased efficiency, and better decision-making in complex environments.

Potential Commercial Applications

Potential commercial applications of this technology include industrial automation, process control systems, and smart grid management.

Possible Prior Art

One possible prior art in this field is the use of traditional control loop management techniques without the integration of RL agents for decision-making.

== What are the limitations of using RL agents in control systems? == One limitation of using RL agents in control systems is the computational complexity involved in training the agents to make optimal decisions in real-time applications. This can lead to increased processing time and resource requirements.

== How can the performance of RL agents in control systems be evaluated and improved? == The performance of RL agents in control systems can be evaluated by analyzing the effectiveness of the actions taken by the agents in moving the environment towards the predefined target. Improvements can be made by fine-tuning the agent's learning algorithms and reward mechanisms to achieve better outcomes.

Original Abstract Submitted

a computer implemented method for managing a plurality of closed control loops, cls, operable in a control system. the method includes using a reinforcement learning, rl, agent, to determine an action to perform in the control system with respect to the plurality of closed cls in the control system. the rl agent is rewarded based on an extent to which an environment on which the control system acts moves towards a predefined target as a result of performing the determined action.