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  6. Embedded AI Control Controller "E-Smart MPC"

Embedded AI Control Controller "E-Smart MPC"

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last updated:Dec 21, 2025

Proxima Technology
Proxima Technology
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Attention equipment manufacturers! We are entering an era where AI integrates control. Elevate your equipment's competitiveness to the next stage with Smart MPC.

An era where energy efficiency and control performance determine the value of equipment. AI × MPC (Model Predictive Control) will revolutionize the precision, flexibility, and energy-saving capabilities that traditional PID control has struggled with. 'E-Smart MPC' is an embedded AI control controller developed based on industrial Raspberry Pi. It resolves the challenges of traditional MPC, such as "difficulty in model construction," "computational load," and "complex tuning," achieving a design that can be easily implemented by equipment manufacturers. - All-in-one design that fits directly into control panels - Intuitive operation via GUI and automatic tuning function - Pre-trained AI that executes optimal control instantly By embedding "intelligence" into the equipment itself, it simultaneously achieves energy reduction, stable operation, and improved maintainability. A new added value of "Smart MPC equipped" for your products. *For more details, please refer to the related links or feel free to contact us.

    Control PanelOther FA equipment
E-Smart_MPC_PiLink_pb_Proxima_top_.png

Embedded AI Control Controller "E-Smart MPC"

E-Smart_MPC_PiLink_pb_Proxima_top_.png
E-Smart_MPC_PiLink_pb_Proxima_top_.png
  • Related Link - https://proxima-ai-tech.com/products/e-smart_mpc.h…
  • Special site - Product/service details - https://pr.mono.ipros.com/proxima/product/

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【Hardware Specifications】 ■Processor: Cortex A76 (4 cores) ■Memory: 8GB RAM ■Storage: 32GB (for system and learning data storage) ■Enclosure: Compatible with control panel storage (compliant with industrial standards) ■Power Supply: DC24V (industrial standard) ■Operating Temperature: 0 to 50℃ ■Network: Gigabit Ethernet × 2 ports ■Communication Protocol: MELSEC Communication Protocol etc. *For more details, please refer to the related links or feel free to contact us.

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Challenges of control faced by manufacturing sites like this: ■ Limitations of PID control ■ Human burden of control adjustments ■ Barriers to the introduction of advanced control technologies *For more details, please refer to the related links or feel free to contact us.

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Next-Generation Smart Control to Reduce 'Control Waste' That Was Difficult to Achieve with PID Control in Air Conditioning, Furnaces, Boilers, etc.

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Due to the soaring energy prices and the need for carbon neutrality, energy reduction has become an urgent issue in the manufacturing industry. However, the currently mainstream PID control is insufficient in responding to complex processes and sudden disturbances, leading to "control waste" caused by excessive control actions and standby operations. Additionally, adjustments require advanced experience, and issues of dependency on specific individuals and black-boxing are also challenges. In this seminar, we will introduce "Smart MPC," which addresses these challenges. This control technology, which combines machine learning and optimization techniques, enables high-precision predictive control by anticipating environmental changes from past operational data, even without specialized knowledge. It significantly reduces energy costs in air conditioning, furnaces, boilers, and more. The embedded "E-Smart MPC," set to be released in July 2025, can be directly mounted onto control panels and allows for quick implementation through GUI operations. If you are involved in the manufacturing industry and are considering energy cost reduction or control optimization, please join us. Date and Time: November 28, 2025 (Friday) 13:00-14:00

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Proxima

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Proxima Technology harnesses the power of mathematics, specifically through mathematical algorithms such as machine learning, optimization, control, and 3D data analysis, to implement solutions in society. We provide AI products that contribute to the operations of everyone, particularly those in the manufacturing industry. The manufacturing environment is one where complex variables intertwine, requiring the maintenance of optimal production processes. We achieve autonomous optimization of production equipment by utilizing real-time data analysis and suitable control algorithms powered by AI.

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