June 3, 2026 — As global manufacturers confront tightening sustainability mandates and volatile energy costs, the industrial automation sector is witnessing a shift in how motion control is architected at the firmware level. ABB's Machine Automation Division (B&R) and Salzburg University of Applied Sciences have jointly filed a patent for an AI-driven energy-optimized motion control system — a breakthrough that could reshape the energy profile of PLC-controlled drive systems across factories worldwide.
The patent, announced this week, targets the core of industrial motion: positioning, acceleration, deceleration, and cyclic movement sequences. These highly dynamic operations, executed millions of times daily in robots, CNC machine tools, and automated production lines, account for a dominant share of factory-floor electricity consumption. The new system uses artificial intelligence to optimize these motion profiles in real time without compromising the precision that PLC-driven automation environments demand.
Analyst Insight: This patent signals a strategic inflection point. Rather than treating energy efficiency as a system-level afterthought addressed through regenerative braking or efficient motors, ABB-B&R is embedding AI-driven optimization directly into the motion control layer — the firmware that governs every movement a drive executes. This bottom-up approach could yield compound savings across entire production lines.
Reinforcement Learning Meets the Factory Floor
The collaboration is anchored at the Josef Ressel Center for Intelligent and Secure Industrial Automation (JRZ ISIA) in Salzburg — a research hub specifically designed to bridge advanced AI theory with industrial-grade implementation. The center's mandate is to develop digital assistants that increase the autonomy of industrial machines through artificial intelligence, and this patent represents one of its most commercially significant outputs to date.
Central to the innovation is a new mathematical formulation of the learning strategy. Traditional reinforcement learning approaches require vast datasets and extended training cycles — luxuries rarely available on live production lines. The patented formulation dramatically reduces both the volume of data and the time needed for the AI to learn optimal motion profiles, making the technology viable for deployment in cyber-physical systems operating under real-world conditions.
How the AI-Optimized Motion Control Works
The system applies multi-objective reinforcement learning to industrial drive control. During operation, the AI continuously evaluates motion sequences — positioning moves, acceleration ramps, deceleration curves, and cyclic patterns — and adjusts parameters to minimize energy draw while maintaining required precision thresholds. Unlike static pre-programmed motion profiles, the AI learns and adapts to the specific mechanical characteristics of each machine, including inertia, friction, and load variations. The key breakthrough is the mathematical formulation that compresses the learning phase, enabling the system to converge on optimal control strategies with significantly fewer iterations than conventional reinforcement learning methods.
Why Energy-Optimized PLC Control Matters Now
The timing of this patent is not coincidental. ABB's own internal studies reveal that more than 70% of a customer robot's carbon footprint stems from electricity consumption during operational life. Meanwhile, a recent Energy Efficiency Movement report found that 58% of global manufacturers cite high energy costs as a direct threat to competitiveness, and over 93% plan to invest in energy efficiency improvements within the next three years.
The global industrial automation market, valued at approximately USD 272.5 billion in 2025, is projected to expand at a CAGR of 9.8% through 2034. Within this growth trajectory, energy efficiency has emerged as a non-negotiable purchasing criterion for PLC and motion control systems. European manufacturers, who commanded a 32.99% market share in 2025, face particularly stringent regulatory pressure under the EU's evolving Energy Efficiency Directive and carbon border adjustment mechanisms.
Industrial Automation & Energy Efficiency: Key Market Data
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Global industrial automation market (2025): USD 272.51 billion
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Projected market size (2034): USD 632.12 billion
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Compound Annual Growth Rate (CAGR): 9.80% (2026–2034)
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Europe market share (2025): 32.99%
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Manufacturers planning energy efficiency investments (next 3 years): 93%
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Manufacturers citing energy costs as competitiveness threat: 58%
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Robot carbon footprint from operational electricity: >70% (ABB internal data)
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Industrial automation & control system market (2026): USD 209.2 billion
Market Trend: PLC manufacturers are increasingly competing on energy intelligence rather than raw processing speed alone. The next-generation PLC is expected to function not merely as a logic controller but as an active energy management node — continuously optimizing power draw across connected drives. ABB-B&R's patent positions the company to lead this transition, potentially influencing firmware roadmaps across its ACOPOS drive family and Automation Studio engineering environment.
From Research Lab to Production Line
Stefan Huber, Head of Research at Salzburg University of Applied Sciences, and Martin Haidacher, Innovation Manager at B&R, have spearheaded the collaboration. The partnership exemplifies a growing model in industrial automation: deep academic-commercial integration where research results are engineered for practical deployment from the outset, rather than being developed in isolation and retrofitted for industrial use later.
The patent covers application across the full spectrum of PLC-controlled drive systems — industrial robots, machine tools, and automated production lines. This breadth suggests ABB-B&R envisions the technology as a horizontal capability embedded across its portfolio, rather than a niche feature for specific high-energy applications.
Frequently Asked Questions
Q: Does the AI system require additional hardware?
The patent describes a mathematical formulation implemented at the control software level, suggesting integration into existing B&R PLC and motion control firmware without mandatory hardware upgrades — though optimal performance may leverage the processing capabilities of newer-generation controllers.
Q: How much energy can be saved?
While specific percentage figures have not been disclosed for this patent, ABB's broader Energy Efficiency Service for robotics has demonstrated up to 30% energy reduction through optimized motion profiles and operational parameters. The patented AI system targets similar optimization vectors at the firmware level.
Q: When will this technology reach commercial products?
The patent filing represents an early-stage milestone. Industrial automation product cycles typically span 18–36 months from patent to commercial firmware release. Given the collaboration's focus on practical implementation, integration into B&R's Automation Studio platform is a logical near-term target.
Q: Is this technology limited to ABB-B&R hardware?
As a joint patent between ABB's B&R division and Salzburg University, the intellectual property is controlled by the patent holders. However, the underlying principles — AI-driven optimization of motion trajectories for energy efficiency — represent an industry-wide research direction being pursued by multiple PLC and motion control vendors.
Implications for the PLC and Motion Control Landscape
For systems integrators and end-users in the Koeed ecosystem, this development carries several implications. First, it signals that energy optimization is moving from a peripheral concern to a core firmware capability — future PLC and motion control procurement decisions will increasingly weigh embedded energy intelligence alongside traditional metrics like scan time, axis count, and communication bandwidth.
Second, the patent's focus on reducing learning data and time addresses one of the most persistent barriers to industrial AI adoption: the gap between laboratory performance and real-world deployability. If ABB-B&R can deliver AI that learns efficiently on live production machinery without extensive commissioning, it lowers the threshold for adoption across small and medium-sized manufacturers — not just automotive-tier enterprises with dedicated AI engineering teams.
Strategic Takeaway: This patent places ABB-B&R in a select group of automation vendors embedding AI natively into motion control firmware. Competitors — including Siemens (Simatic/Sinamics), Rockwell Automation (Kinetix), and Mitsubishi Electric (MELSERVO) — are pursuing parallel AI-enhanced motion strategies, but ABB-B&R's academic partnership at JRZ ISIA provides a structured pipeline for translating research into patented, protectable commercial technology. The race to own the energy-intelligent PLC stack is now formally underway.
The patent does not specify a commercialization timeline, but given B&R's track record of integrating research outputs into its Automation Studio engineering platform and ACOPOS drive family, industry observers anticipate firmware-level implementations could appear within two to three product cycles. For an industry where energy now competes with throughput as the defining operational metric, that timeline may prove to be exactly what the market demands.
