Hopfield Neural Networks for Online Constrained Parameter Estimation with Time-Varying Dynamics and Disturbances

TL;DR

This paper introduces two Hopfield neural network estimators for real-time constrained parameter estimation in systems with time-varying dynamics and disturbances, ensuring boundedness and adaptive tuning.

Contribution

The paper presents novel projector-based HNN estimators that handle constraints and disturbances, with proven stability, explicit tuning rules, and an adaptive identifiability monitor.

Findings

Both estimators achieve global uniform ultimate boundedness.

Explicit convergence rates and steady-state bounds are derived.

An adaptive monitor improves estimation robustness in poorly excited conditions.

Abstract

This paper proposes two projector-based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time-varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint-aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the constrained least-squares target. The second augments the state with compensation neurons and a concatenated regressor to absorb bias-like disturbance components within the same energy function. For both estimators we establish global uniform ultimate boundedness with explicit convergence rate and ultimate bound, and we derive practical tuning rules that link the three design gains to closed-loop bandwidth and steady-state accuracy. We also introduce an online identifiability monitor that adapts the constraint weight and time step, and, when needed, projects updates onto identifiable subspaces to prevent drift in poorly excited directions...