Autonomous Control AI Service for Use with Edge Controllers Launched by Yokogawa

Author photo: Shin Kai
By Shin Kai
Company and Product News

Yokogawa Electric Corporation announced the launch of a reinforcement learning service for edge controllers. This autonomous control service for OpreX Realtime OS-based Machine Autonomous Control AI ServiceControllers (e-RT3 Plus) utilizes the Factorial Kernel Dynamic Policy Programming (FKDPP) reinforcement learning AI algorithm and consists of packaged software and an optional consulting service and/or a training program, depending on end user requirements. This software is being released globally, while consulting and the training program will be provided first in Japan, then in other markets.

The autonomous control AI is a new technology for situations that achieves complex control and eliminates the need to rely on manual operations.

FKDPP is a new control technology that is different from PID control and APC. In March 2022, it was announced that Yokogawa and JSR Corporation's elastomer business unit (now owned by ENEOS Materials) had successfully concluded a 35 day field test in which AI was used to autonomously control a facility in a chemical plant that could not be controlled using existing control methods and had necessitated the manual operation of control valves based on the judgements of plant personnel. A world first, this was accomplished despite the presence of factors, such as weather conditions, that could have significantly disrupted the control state.

With the new service that Yokogawa is announcing, customers can create AI control models using the FKDPP algorithm and install them on edge controllers. This service has the following features and merits.


  • Thanks to simplification of the AI model creation process, even non-AI experts can create an autonomous control AI model and install it on an e-RT3 edge controller.
  • Retrofit of edge controllers with the installation of the autonomous control AI can be performed while other facilities remain in use.
  • Supports control cycles as short as 0.01 seconds and is optimal for device control applications that require a quick response.


  • Enables autonomous control where only manual control was possible: By applying autonomous control AI in areas that are beyond the capabilities of PID control and APC, both autonomy and optimal control can be achieved. It enables stable control that is less susceptible to external disturbances and increases productivity.
  • Suppresses overshoot: Although this will vary depending on the control targets, FKDPP suppresses overshoot. The reduction of overshoot (a condition where a set value is exceeded) is expected to extend, for example, the lifetime of furnaces and other heating facilities by reducing unnecessary overheating.
  • Significantly reduces settling time: FKDPP significantly reduces the settling time compared with PID control, saving energy and improving productivity.
  • Ability to achieve the right balance between conflicting requirements: Although this will depend on the control targets, FKDPP is able to resolve conflicting requirements and, for example, achieve the right balance between the need to reduce energy use while maintaining product quality.

To use this system, edge controllers (sold separately), access to the autonomous AI learning service, a software package for the implementation of AI control models on edge controllers, and a license to run AI control models are required. Depending on the application, training programs, related consulting services, engineering services, and more are available to help users get started.

Primary targeted markets include resources and energy (petroleum, chemicals, natural gas, electric power, renewable energy), materials (textiles, pulp and paper, paints), electronics (semiconductor manufacturing equipment), food and agriculture, pharmaceuticals, and water and wastewater for the use of control of temperature, pressure, water level, flow, etc.

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