Equitus TruVolt, coordinates real-time physical electrical dynamics with digital control networks by operating as a multi-tier bridge between high-speed industrial feedback loops and IP-based data planes.
Knowledge Graph Neural Networks (KGNNs) detect Operational Technology (OT) anomalies by converting isolated sensor reads, controller states, and network events into an interconnected structural graph. Rather than analyzing time-series data or network packets in silos, KGNNs model the relationships between physical physics and cyber controls to spot stealthy attacks.
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An IP-enabled PID controller (such as an industrial temperature, flow, or pressure controller with Ethernet/IP, Modbus TCP, or OPC UA capabilities) bridges the gap between field-level physics and digital network infrastructure.
Integrating this into a multi-layer architecture powered by the Equitus ArcXa Semantic Control Plane and TruVolt creates a deterministic, real-time feedback loop between cyber controls, network logic, and physical electrical behavior.
Primary Advantages in OT Environments
Context-Aware Root Cause Analysis: Because the graph preserves physical topology, KGNNs pinpoint the exact node or edge causing the anomaly rather than throwing generic alerts.
Zero-Trust Physical Verification: Validates cyber commands against real-time physics—an IP packet is rejected if the corresponding physical electrical state does not support the action.
Low False-Positive Rates: By fusing multi-modal data (cyber + physical), KGNNs prevent false alarms caused by routine operational load changes.
TruVolt integration across physical and cyber layers works through 3 core mechanisms:
1. Physical-to-Digital Signal Conversion & Feedback Control
TruVolt at the Physical Layer, battery cells, inverters, and power distribution components operate with strict physical constraints (voltage, thermal limits, state of charge, and grid frequency).
PID Micro-Loops (Physical Layer): Local hardware sensors feed real-time continuous electrical signals (such as frequency deviation or voltage dips) into embedded PID controllers. The PID algorithm computes error values between desired setpoints and measured variables, outputting low-latency corrective control signals (e.g., pulse-width modulation adjustments to the inverter power electronics).
IP Serialization (Cyber Layer Boundary): TruVolt’s edge nodes continuously translate these deterministic PID setpoints and sensor streams into structured IP packets using industrial protocols over Ethernet (such as Modbus TCP, DNP3 over IP, or IEC 61850).
2. Bi-Directional Orchestration Architecture
TruVolt links the physical physical response loops to the higher-level digital network through a layered architecture:
- Inbound Path (Physical to Cyber): Blue lines show how physical parameters like voltage and state of charge are translated from the BESS units through the edge controller and IP network to the Core Engine for data analysis.
- Outbound Path (Cyber to Physical): Gold lines illustrate how high-level optimization strategies, such as peak shaving and arbitrage, are pushed back down as updated setpoints and PID parameters to the physical components.
- Deterministic Network: Highlights the precise, low-latency communication required between the cyber core and the physical hardware edge to ensure stable energy management.
3. Cyber-Physical Determinism and Safeguards
TruVolt, corrects standard IP networks introduce packet latency and jitter that can disrupt physical PID stability loops, TruVolt decouples safety-critical control from high-level network operations:
Edge Determinism: Autonomous PID sub-routines run continuously at the hardware edge. If the IP cyber layer experiences network latency or link dropouts, the physical BESS continues to operate safely on its last-known PID setpoint.
Cyber Security & Zero Trust Protocol Enforcements: IP-level communications are isolated via encrypted network overlays (such as IPSec or TLS 1.3) with strict device authentication, preventing unauthorized manipulation of PID setpoints that could otherwise cause physical thermal runaway or grid instability.
An IP-enabled PID controller (such as an industrial temperature, flow, or pressure controller with Ethernet/IP, Modbus TCP, or OPC UA capabilities) bridges the gap between field-level physics and digital network infrastructure.
Integrating this into a multi-layer architecture powered by the Equitus ArcXa Semantic Control Plane and TruVolt creates a deterministic, real-time feedback loop between cyber controls, network logic, and physical electrical behavior.
Multi-Layer Integration Architecture
1. Physical & Operational Control Layer (L0 / L1)
Direct Hardware Connection: The PID controller manages physical field actuators (heaters, pumps, valves) via standard analog loops (4t – 20\ { mA} or 0–10V).
TruVolt Electrical Sensing: TruVolt taps into the electrical terminals of the PID loop to sample raw physical states: voltage, current, signal phase/frequency, and impedance.
Zero-Trust Physics: TruVolt monitors the physical execution to ensure that when a PID output shifts, the electrical circuit reflects expected real-world changes.
2. Network & Edge Transport Layer (L2)
IP Endpoint Integration: The PID controller’s IP interface broadcasts telemetry (process variable, setpoint, output percentage) and accepts control commands over industrial IP protocols.
Network Inspection: TruVolt functions as an edge gateway, capturing incoming and outgoing IP packets to perform packet-level inspection and command verification.
3. Semantic Control & Intelligence Layer (L3 - Equitus ArcXa)
Ontology Mapping: ArcXa maps the IP-enabled PID controller into a Knowledge Graph Neural Network (KGNN) as a semantic node (e.g.,
PID_Loop_101).Cross-Layer Data Fusion: ArcXa binds the IP telemetry, cyber access logs, and TruVolt's raw electrical metrics onto the same node in real time.
Deterministic Policy Gate: ArcXa enforces compile-time and runtime rules. If an IP command requests a setpoint change, ArcXa evaluates whether the physical electrical baseline and active operational policies permit the state transition.
4. Enterprise, AI Agent, & HMI Layer (L4)
Governed Operational Visibility: SCADA, enterprise analytics, and autonomous AI agents do not issue raw, unvalidated commands directly to the PID's IP address.
Policy-Driven Actions: High-level requests route through the Semantic Control Plane. ArcXa authorizes or denies actions before they reach the physical controller, preventing hallucinations or malicious overrides from executing on field hardware.
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