| name | data-driven-distributed-control |
| description | Data-driven distributed controller synthesis using spatial regret optimization. For synthesizing optimal distributed controllers directly from frequency-response data without requiring a parametric system model. Use when: (1) designing distributed control systems from experimental data, (2) comparing spatial regret vs H2/Hinf performance, (3) building data-driven controllers with communication structure constraints, (4) model-free controller synthesis for networked systems.
|
Data-Driven Distributed Control via Spatial Regret
Core Methodology (arXiv:2605.02506)
Synthesize optimal distributed controllers directly from frequency-response data using
spatial regret — measures performance gap between a structured distributed controller
and an oracle with enhanced communication topology.
Key Concepts
Spatial Regret
- Quantifies the cost of distributed communication constraints
- Compares structured controller performance against an oracle with richer communication
- Relaxes topology assumptions: oracle can use any enhanced structure
- Provides a principled trade-off between communication cost and control performance
Data-Driven Synthesis
- Uses experimentally obtained frequency-response data (no parametric model needed)
- Preserves stability and desired communication structure
- Iterative solution (not single convex program) due to relaxed oracle assumptions
- Outperforms classical H2/Hinf designs in numerical benchmarks
Workflow
Step 1: Collect Frequency-Response Data
Obtain G(jw) from experiments or identification at discrete frequencies.
Step 2: Define Communication Structure
Specify which subsystems can communicate via structural constraint matrix S
(S[i,j]=1 means subsystem i can access subsystem j measurements).
Step 3: Solve Spatial Regret Problem (Iterative)
- Initialize controller K with desired structure
- Compute oracle K_oracle with relaxed constraints
- Minimize regret: min_K [J(K) - J(K_oracle)]
- Iterate until convergence
Step 4: Validate
- Check stability margins
- Compare H2/Hinf performance metrics
- Verify communication structure preservation
Comparison: Spatial Regret vs Classical Methods
| Criterion | H2/Hinf | Spatial Regret |
|---|
| Model required | Yes (parametric) | No (frequency data) |
| Communication constraints | Hand-fixed | Explicitly optimized |
| Oracle comparison | None | Built-in |
| Conservatism | High | Reduced |
| Computation | Single program | Iterative |
Pitfalls
- Iterative solution may not converge for ill-conditioned systems
- Frequency data quality critically affects synthesis result
- Oracle definition must be carefully chosen (too relaxed = trivial regret)
Reference
arXiv:2605.02506 — Gupta, Martinelli, Ferrari-Trecate, Furieri, Karimi (2026)
