Autonomous Solar Array Pointing and Optimization Workflow

The workflow ingests and fuses real-time data from star trackers, sun sensors, IMUs, and onboard ephemeris models to calculate the spacecraft's precise attitude and the sun's vector relative to the array plane. This fusion agent creates a single source of truth for pointing calculations, eliminating errors from any single degraded sensor and ensuring orientation commands are based on high-confidence data.

A core optimization agent runs a model-predictive control loop, simulating power generation and thermal loading over the next orbit. It balances the goal of maximum energy harvest against the risk of overheating components or exposing sensitive instruments. The agent outputs an optimal, time-varying pointing profile that may intentionally detune from perfect sun-tracking to manage thermal budgets and extend hardware life.

A dedicated monitoring agent cross-references the planned array slew path with onboard geometric models of the spacecraft bus, deployed instruments, and communication antennas. It uses these models to predict shadowing or physical collisions, issuing corrective hold or alternative slew commands. This prevents self-induced power drops or mechanical damage, a critical safeguard when ground intervention is delayed by hours or days.

The optimized pointing profile is translated into torque commands for reaction wheels or control moment gyros (CMGs). The workflow integrates directly with the ACS via a dedicated API or bus message. A feedback loop confirms actuation completion and validates the new array attitude against sensor data, creating a closed-loop control system that operates independently of ground stations.

Every decision and sensor reading is logged with timestamps and confidence scores. Anomaly detection agents monitor for unexpected power drop-offs, actuator performance degradation, or constraint violations. Upon detection, the system can trigger a fallback to a conservative, sun-tracking-only mode while compiling an anomaly report for priority downlink. This ensures auditability and provides ground teams with actionable diagnostics.

While autonomous, the system is governed by uploadable policy files that define absolute constraints (e.g., 'never point array within 20 degrees of Instrument X'). Ground control can issue manual pointing overrides or temporarily disable the optimization engine via a secure command uplink. This human-in-the-loop layer is essential for contingency operations, testing, and mission re-planning.

This team owns the spacecraft's orientation and provides the core pointing telemetry (sun vector, body rates) and actuator commands (reaction wheels, torque rods). The custom workflow integrates directly with their flight software via a dedicated API, consuming real-time attitude data and sending optimized gimbal drive requests. Their sign-off is required on all control loop stability and fault-handling logic to prevent unsafe torques or platform instability.

Responsible for the spacecraft's energy budget, this team defines the optimization objectives (maximize current, avoid battery overcharge, manage thermal loads). The workflow's AI agent must ingest their power telemetry (bus voltage, battery state of charge) and constraints (max array temperature). They validate that the autonomous scheduler's decisions protect hardware and meet the daily energy margin needed for payload operations.

This team integrates the custom pointing agent into the spacecraft's real-time operating system and middleware (e.g., NASA cFS, ESA RTEMS). They handle the low-level device drivers for the solar array drive actuators, manage the workflow's compute resource allocation, and implement the watchdog timers and health monitoring required for flight certification. The architecture typically uses a containerized or partitioned process to isolate the AI logic.

The end-users of the system. They require a configurable human-in-the-loop layer with override commands, telemetry dashboards showing optimization performance, and predefined contingency modes. The workflow must produce explainable logs of its decisions (e.g., 'slew paused due to thermal limit') for anomaly resolution. Their buy-in is critical for operational acceptance and defining the handover protocol from ground-commanded to autonomous control.

Solar array efficiency degrades with temperature, and improper pointing can cause localized overheating. This team provides the thermal models and hard limits (e.g., max panel temperature) that the optimization agent must respect. The workflow architecture includes a thermal prediction sub-agent that forecasts temperature rise for a given sun angle, enabling proactive slew adjustments to balance power generation with thermal safety.

This cross-functional team owns the overall requirements, verification matrix, and failure modes and effects analysis (FMEA). They ensure the autonomous workflow has clearly defined operational domains, fallback behaviors (e.g., default to a sun-tracking law if the AI agent faults), and does not create single-point failures. Their approval gates are mandatory before the software can be uploaded to flight hardware.