System Identification › Key Parameters

The key parameters for the identification implementations are:

• proc_std: Standard deviation of injected process noise (0.01) in the drone trajectory, including its held-out tail. It sets a per-step expected squared-error contribution; the irreducible multi-step rollout error is horizon dependent because new disturbances accumulate along the trajectory.
• r, c: Row and column counts of the Hankel matrix (both 8). For a minimal order-$n$ deterministic system, $r \ge n$ and $c \ge n$ support a full-rank Hankel factorization under sufficient excitation, while recovering the state-transition operator from a shifted Hankel requires enough adjacent blocks. Here 8 is comfortably above the required depth and deliberately generous for a numerical-rank check; it is not a minimal or "twice the order" prescription.
• sv_threshold: Relative singular-value cutoff (1e-8) declaring numerical rank and thereby the estimated system order.
• Excitation gains (0.8, 0.6): Shaping-filter coefficients coloring the simulation input; their choice governs whether persistent excitation holds.
• ARX lags: Number of lagged outputs and inputs entering the regressor (two each here); this fixes the asserted model order.
• DT: Integration timestep of the pendulum environment (0.05), tying discrete transitions to continuous dynamics.
• hidden_dims: Widths of the feedforward dynamics network (6 -> 64 -> 64 -> 2 with Tanh units).
• lr: Adam learning rate (1e-3) used for the network fit.
• epochs: Number of full-batch training iterations (400).