Sankalp Thakur / Industrial Edge Setpoint Lab

Four tanks. Two pumps.
Changing dynamics.

Inspect PI, multivariable MPC and two compact kernel policies on a recognized nonlinear process model. Fresh policy rollouts alone did not fix this kernel adaptation’s tracking failures.

Synthetic Johansson equations · full four-level observations assumed · 288 held-out episodes · 20 minutes per episode · saved results, no plant connection

PI / mean tracking error
—
Root mean square error, cm
MPC / mean tracking error
—
Root mean square error, cm
MPC change from PI
—
Data per learning mode
17,280
Transitions · same interaction budget

Two lower-tank targets

One confirmation episode per condition (seed 63001). The aggregate table uses all 12 episodes.

PIMPCOffline kernel policyPolicy rollout kernel policyReference (dashed)

Held-out outcomes

ControllerMean RMSE, cm ↓Stage cost ↓Samples outside envelopeMean episode p95, msTotal solver failures
The level monitor checks 0.2–25 cm at five-second samples across all four tanks. Input and slew limits do not guarantee a level envelope. Host timings exclude acquisition, communication and PLC scans. Voltage squared is a proxy, not measured electrical energy.

What the comparison tests

Minimum-phase PI pairs each lower tank with its direct pump. Nonminimum-phase PI crosses those pairings, following the relative gain array. MPC predicts every future move using the known nominal model and enforces pump-voltage and rate limits across the horizon.

Both kernel policies share the same initial data, 64-state dictionary, preprocessing and 12 update rounds. One uses fixed random-action data; the other gathers fresh softmax policy rollouts and retains earlier samples.

This is a disclosed FKDPP adaptation. It replaces the original kernel-subset selection with fixed Nyström features, changes the task and reward, and evaluates greedy policies. One learning-seed sequence does not establish architecture-wide performance.

What changes in the plant

The controller continues to use the nominal configuration when a hidden change occurs. The experiment measures that failure mode; it does not implement configuration detection or recovery.

Any operational application would need measured valve/configuration feedback, authorized local admission, independent calibration, local interlocks and readback. Generic source and simulation results do not establish operating-site readiness.

Inspect and reproduce

Source, both policies, all tuning candidates, episodes and receipts ↗ · Benchmark report ↗

Protocol and selected tuning

The protocol was frozen before tuning and training. Training, validation and confirmation seeds are disjoint. The table retains every selected controller’s failures.

Protocol SHA256