Modular Sign Compensation for MIMO Systems with Unknown Control Direction: An Exact Nominal Recovery Approach

What the math gives to ML

The paper provides a constructive sign-compensation wrapper for systems whose input channels may be multiplied by unknown, time-varying diagonal signs. Its transferable mechanism is modular: retain a nominal controller or update rule, enumerate bounded sign transformations, and use a nominal Lyapunov quantity to detect when a candidate produces the expected dissipation. Once the true sign pattern remains constant long enough, the scheduler traps the correct pattern and exactly recovers nominal closed-loop behavior rather than merely compensating uncertainty with large gains. A promising neural-network transfer is a sign-search wrapper for learned optimizers operating with unknown parameter-block orientations, stale gradients, actuator reversals, or federated client sign inconsistencies.