Why this matters now
As explored in his recent article in Control Engineering, Emerson’s Claudio Fayad—drawing on more than 30 years of industry expertise—explains that software-defined control addresses the limits of embedded, hardware-centric designs without forcing rip-and-replace. As global competition intensifies, operations teams are pushing for every increment of efficiency. Many controllers now sit in server rooms rather than harsh environments, which changes design tradeoffs and exposes the limits of traditional approaches when plants want analytics, AI, and enterprise connectivity.
- Traditional embedded controllers can constrain analytics/AI and scalability needs.
- Software-defined control combines deterministic control with operational workloads and virtualization-based resilience.
- Most plants will modernize in hybrid fashion, integrating software-defined controllers with existing systems.
How are evolving plant requirements exposing the limits of hardware-only control?
Efficiency, agility, and data mobility have become core to competitive advantage. As requirements broaden to include analytics, AI, and cloud connectivity, embedded-only designs can struggle under memory/CPU constraints and rigid upgrade paths. This is especially true now that many controllers run in server-room conditions, reducing the need for heavy hardening and opening the door to new architectures. Claudio shares,
“A wide variety of new operational and business requirements have driven a need for a paradigm shift in industrial controller design. More complex operations, shifting plant configurations, and a need for seamless data mobility and flexibility are inspiring process manufacturers to explore a new way of controlling plants via software-defined control.”
Takeaway: New operational demands outstrip what many embedded controllers were designed to do.
What is a software-defined controller—and why does it matter?
In the DeltaV™ DCS ecosystem, software-defined controllers like the DeltaV IQ Controller use real-time virtualization to deliver deterministic control while also running operational workloads (e.g., analytics, AI) close to the process. The approach leverages modern server hardware, expanding functionality beyond the limits of hardened devices and making capacity and features easier to scale.
- Runs real-time control + operational workloads together
- Taps modern CPUs/memory instead of fixed, embedded constraints
- Scales features more easily than traditional hardware
Takeaway: Software-defined control modernizes compute at the controller layer while preserving deterministic performance.
How does virtualization improve uptime and simplify maintenance?
With virtualization and workload orchestration, Claudio explains, teams can,
“upgrade one server while all the controllers run on another server. When the upgrade is complete, the team can shift controllers back to the updated server and update the other server—all of which can be done automatically with the assistance provided by modern workload orchestration.”
The same architecture enables automatic failover if a controller experiences an issue, improving continuity and reducing unplanned downtime.
Takeaway: Orchestration-assisted upgrades and failover increase resilience without adding engineering complexity.
Where do traditional controllers still fit?
Many plants will continue to deploy control hardware in harsher environments or where legacy systems are already performing well. The DeltaV IQ Controller is designed to integrate with traditional controllers, enabling new capabilities without a wholesale replacement of installed assets.
Takeaway: The practical path forward is hybrid—augment, don’t uproot.
How can teams adopt software-defined control without disruption?
Organizations can start by adding the DeltaV IQ Controller alongside existing DeltaV DCS systems where analytics/AI and flexibility provide immediate value. Co-locating workloads at the controller and leveraging virtualization builds redundancy and scalability while preserving familiar tools and processes.
