# Loop Transfer Recovery > Use when you must run loop-transfer-recovery on the LQG loop of the two-state plant family: inflate the filter process noise weight q on the driven input channel through Qw(q) = Qw0 + q^2 B B^T, re-solve the filter Riccati equation for the error covariance and the estimator gain at every q, and compare the recovered output loop transfer function with the full-state target loop on a log-spaced frequency grid until the grid-max mismatch M(q) falls at or below the recovery tolerance. Produces the full-state target loop samples, the mismatch-versus-q recovery trace, the recovery gain q* first meeting the tolerance, the recovered filter covariance and estimator gain, and the recovery verdict that gate the loop-shaped LQG design. Trigger: loop-transfer-recovery, LTR, full-state-loop-recovery, lqg-loop-shaping, recovery-gain-tuning, target-feedback-loop. - Skill: `ashfordeou/loop-transfer-recovery` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ashfordeou/loop-transfer-recovery` - Raw SKILL.md: https://api.skillmd.com/api/skills/ashfordeou/loop-transfer-recovery/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - License: Apache-2.0 - Author: ashfordeOU (https://skillmd.com/u/ashfordeou) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ashfordeou/loop-transfer-recovery --- # Loop Transfer Recovery (gnc-autonomy/optimal-control/loop-transfer-recovery) Use when the task is output-side loop-transfer recovery (LTR) on the LQG loop of the canonical scalar-input two-state plant family: reshaping the recovered output loop toward the full-state target loop by inflating the filter process noise weight on the driven input channel, the Doyle-Stein 1981 recovery construction (textbook treatment Maciejowski, Multivariable Feedback Design, 1989, chapter 5). This leaf implements the exact closed forms of both algebraic Riccati equations of the family in pure Python, stdlib only, and evaluates the loop transfer samples with the builtin complex type. It pairs with gnc-autonomy/optimal-control/lqg-design, which consumes the recovered estimator gain L(q*) as the filter gain input of its compensator assembly, and with gnc-autonomy/optimal-control/lqr-design, which owns the regulator gain design trade this leaf treats as a fixed full-state target ingredient. The adjacent gnc-autonomy/control/frequency-response-design leaf measures the margins of a given open loop transfer function and never synthesizes or recovers a loop; here the loop-shape match is judged by the grid-max complex mismatch M(q), never by margin numbers. ## Domain quick reference - Plant family: A = [[0, 1], [0, -a]] with damping a >= 0 (a = -A[1][1]), input B = [0, 1] (acceleration-like control on the rate state) and measured output C = [1, 0] (y = x1, position-like). Unit convention: x1 in m (or rad), x2 in m/s (or rad/s), u in m/s^2 (or rad/s^2), omega in rad/s, q dimensionless; the weights Q = diag(q1, q2), R = r, Qw = diag(w1, w2) and Rw = rv sit in the consistent squared units. Plant transfer G(s) = C (sI - A)^-1 B = 1 / (s (s + a)). - Regulator algebraic Riccati equation: A'P + PA - P B R^-1 B' P + Q = 0. With P = [[p1, p2], [p2, p3]] the exact scalar reduction shared with the lqr-design and lqg-design siblings is p2 = sqrt(r q1), p3 = r (-a + sqrt(a^2 + (2 p2 + q2) / r)) and p1 = a p2 + p2 p3 / r; the full-state gain for u = -K x is K = [p2 / r, p3 / r]. - Filter (Kalman) algebraic Riccati equation: A S + S A' - S C' Rw^-1 C S + Qw = 0. Its entries: s2 is the root of u^2 + 2 a^2 rv u + 2 a u sqrt(rv (2 u + w1)) - rv w2 = 0 on (0, sqrt(rv w2)], exactly sqrt(rv w2) at a = 0 and bisection to 1e-15 relative otherwise, then s1 = sqrt(rv (2 s2 + w1)) and s3 = a s2 + s1 s2 / rv; S = [[s1, s2], [s2, s3]] and the estimator gain L = [s1 / rv, s2 / rv]. - Recovery inflation: Qw(q) = Qw0 + q^2 B B^T touches only the (2,2) entry because B B^T = [[0, 0], [0, 1]], so w2(q) = w2 + q^2: the fictitious noise on the driven state grows with the SQUARE of q. - Target loop (the full-state loop at the plant output, SISO-identical to the input break): L_t(s) = K (sI - A)^-1 B, family closed form (k1 + k2 s) / (s (s + a)). - Recovered output loop of the LQG structure with filter gain L(q): L_lqg(s; q) = G(s) K (sI - A + BK + L(q)C)^-1 L(q). The 2x2 matrix path inverts [[s + l1, -1], [k1 + l2, s + a + k2]] by its adjugate; the family rational closed form is K Phi_o L = num / den with num = (k1 l1 + k2 l2) s + k1 (a l1 + l2) and den = s^2 + (l1 + a + k2) s + (a + k2) l1 + k1 + l2. - Frequency grid: N_FREQ log-spaced points w_k from OMEGA_MIN to OMEGA_MAX, w_k = OMEGA_MIN * exp(ln(OMEGA_MAX / OMEGA_MIN) * k / (N_FREQ - 1)) for k = 0 .. N_FREQ - 1. - Mismatch metrics at q (denominator the grid-max target magnitude): M(q) = max_k |L_lqg(j w_k; q) - L_t(j w_k)| / max_k |L_t(j w_k)| and Mmag(q) = max_k ||L_lqg(j w_k; q)| - |L_t(j w_k)|| / max_k |L_t(j w_k)|. - Recovery rule: q* is the FIRST q of the geometric sweep q_min, q_min * step, ... (inclusive up to q_max) with M(q) <= tol; without a meeting point the verdict is False and q* is None. The trace strictly decreases in M over the sweep for the minimum-phase canonical family. - ARP4754A (reference-only) frames development assurance for aircraft systems; the Riccati and recovery mathematics is common control-theory knowledge, summary only. ## Workflow 1. Fix the plant and the weights: canonical family damping a, regulator weights Q = diag(q1, q2) and R = r, nominal filter noise covariances Qw0 = diag(w1, w2) and Rw = rv, and the frequency band; build the log-spaced frequency grid with frequency_grid(omega_min, omega_max, n_freq). 2. Solve the regulator Riccati equation for the full-state target ingredient with regulator_riccati(a, q1, q2, r): returns the symmetric P and the gain K = [k1, k2] for u = -K x. 3. Form the full-state target loop samples with target_loop(j w, a, K) over the grid and read off the grid-max target magnitude; the target band sits around the unity-magnitude crossing w_c. 4. Solve the nominal filter Riccati equation with filter_riccati(a, w1, w2, rv) for the error covariance S0 and the estimator gain L0 (the s2 root is closed form sqrt(rv w2) at a = 0 and bisection at a > 0), and measure the nominal LQG loop mismatch at q = 0 with loop_mismatch(0, a, K, grid): the estimator lag and roll-off bend the loop far from the target. 5. Run the recovery gain sweep with recovery_sweep(a, K, grid, tol, q_min, q_max, step): at every q, inflate the filter process noise weight on the driven input channel through inflated_noise_covariance (q), re-solve the filter Riccati equation for S(q) and L(q), evaluate the recovered output loop with recovered_loop(j w, a, K, L) against the target samples, and record (q, M(q), Mmag(q), l1, l2). 6. Read off the recovery gain q* and the recovery verdict from recovery_result(...): q* first meeting the tolerance, the recovered filter covariance S(q*) and estimator gain L(q*), the mismatch values at q* and the improvement ratio over the nominal loop. 7. Confirm the deterministic checks with the contract test scripts/test_loop_transfer_recovery.py (stdlib-only, offline, runs under both /usr/bin/python3 and the pyenv 3.13.12 interpreter). ## Worked example Worked scenario: the canonical undamped double-integrator plant a = 0 with the matched weights of the pack (the lqg-design Example A weights): Q = diag(1, 1), R = 1, Qw0 = diag(1, 1), Rw = 1. The frequency grid holds 41 log-spaced points over [0.1, 20] rad/s, the full-state target loop crosses unity magnitude at w_c = 1.8173540210239707 rad/s, and the recovery sweep runs q = 10, 100, ..., 1e8 (8 points) with the gate RECOVERY_TOL = 1e-3. All values below are real module outputs (scripts/loop_transfer_recovery_logic.py), bitwise identical under both interpreters and inside the spec magnitude bounds. - Target design (the fixed full-state ingredient): regulator ARE P = [[1.7320508075688772, 1], [1, 1.7320508075688772]] (= sqrt(3) on the diagonal), K = [1, 1.7320508075688772] = [1, sqrt(3)], ARE residual 4.441e-16. Target samples L_t(j w) = -(1/w^2) - j sqrt(3)/w with grid-max magnitude 101.48891565092218 at w = 0.1 rad/s. - Nominal LQG loop before recovery (q = 0): S0 = P, L0 = [1.7320508075688772, 1] = [sqrt(3), 1]; its loop transfer is L_lqg(s; 0) = (1 + 2 sqrt(3) s) / (s^2 (s^2 + 2 sqrt(3) s + 5)), and M(0) = 0.79265793913710081, Mmag(0) = 0.79152889018535089: the nominal output loop is off the full-state loop by up to 79% of the target peak. - Recovery trace (q, M(q), Mmag(q), L(q) = [l1, l2]) over the sweep: q = 10: M = 0.46672494862660469, Mmag = 0.46665926627057602, L = [4.5934465537591462, 10.04987562112089] q = 100: M = 0.20156746815359272, Mmag = 0.20156524575736853, L = [14.177799538363226, 100.00499987500625] q = 1000: M = 0.072062021956610434, Mmag = 0.072062015642601701, L = [44.732549670231741, 1000.000499999875] q = 1e4: M = 0.023772073268366271, Mmag = 0.023772060968471405, L = [141.42489208056693, 10000.000050000001] q = 1e5: M = 0.0076215835906494403, Mmag = 0.0076215752257677788, L = [447.21471354372943, 100000.00000499999] q = 1e6: M = 0.0024207706165423701, Mmag = 0.0024207674026657765, L = [1414.2139159267949, 1000000.0000005] q = 1e7: M = 0.00076658259948715081, Mmag = 0.0007665815223384054, L = [4472.1360668029884, 10000000.00000005] q = 1e8: M = 0.0002425216736106494, Mmag = 0.00024252132677611768, L = [14142.135659086289, 100000000] M(q) strictly decreases over the sweep and the complex and magnitude-only mismatches track to five digits at every q: recovery reshapes magnitude and phase together. The late-decay ratio M(1e8)/M(1e7) = 0.316367 sits within 0.02 of 1/sqrt(10): the M ~ c / sqrt(q) law of the recovery error. - Recovery verdict: q* = 10000000.0 (first sweep q with M(q) <= 1e-3), M(q*) = 0.00076658259948715081, Mmag(q*) = 0.0007665815223384054, so the recovered output loop matches the full-state target loop to better than 7.7e-4 of the peak magnitude across the whole grid; the improvement over the nominal LQG loop is M(0)/M(q*) = 1034.015. At q* the filter solution is S(q*) = [[4472.1360668029884, 10000000.00000005], [10000000.00000005, 44721360668.030106]] with L(q*) = [4472.1360668029884, 10000000.00000005]; the inflation law is w2(q*) = 1 + 1e14, and l2(q*) = sqrt(1 + q^2), l1(q*) = sqrt(2 l2(q*) + 1) exactly. The filter ARE residual at q* is 3.725e-09 (max entry), tiny against entries of order q^2. The price of recovery is estimator speed: the filter characteristic polynomial s^2 + l1 s + l2 has roots of magnitude (1 + q^2)^(1/4) = 3162.28 rad/s at q*, about 1740 times the target crossing, the standard LTR noise-bandwidth trade. - Recovered loop against target at q*: at w = 0.1 rad/s |L_lqg| = 101.41111612 against |L_t| = 101.48891565092218 (nominal 21.157506880); at w = 1.0, |L_lqg| = 1.9988388170 (nominal 0.68138514387); at w = 20, |L_lqg| = 0.086593884562 (nominal 0.00043194562704). Complex agreement at w = 1.0: L_lqg(q*) = -0.99999970038576613 - 1.7307100322379296j against L_t = -1 - 1.7320508075688772j. - Damped cross-checks: filter_riccati(0.5, 1.0, 4.0, 1.0) reproduces the lqg-design Example B solution (the a > 0 bisection branch). On the damped plant a = 0.5 with the matched weights, K = [1, 1.3027756377319946], and M(0) = 7.1197214039e-01 drops to M(1e5) = 5.7834333689e-03: recovery through the bisection branch reduces the mismatch by a factor of 123. - Determinism: a second identical recovery_result run is bitwise identical in the trace, q_star, S_star and L_star. ValueErrors: 12 exercised rejection cases all raise (negative damping, zero or negative weights, negative q, degenerate frequency bands and sweep bounds). ## Verification - Confirm the worked scenario: recovery_result() returns q_star = 1e7 within 1e-12, M(q*) and Mmag(q*) within 1e-6 relative, S(q*) and L(q*) within 1e-6 relative per entry, improvement ratio 1034.02 within 1e-3 relative and verdict True. - Confirm the grid: 41 log-spaced points over [0.1, 20] rad/s with w_0 = 0.1, w_40 = 20 within 1e-12 and the consecutive-point ratio equal to (20/0.1)^(1/40) within 1e-12. - Confirm the identities: the target loop matrix path equals -(1/w^2) - j sqrt(3)/w at every grid point within 1e-12 (anchor max abs diff 3.553e-15); the recovered loop matrix path equals num / (s^2 den) within 1e-9 (anchor 9.379e-13 at q = 1000); the two SISO multiplication orders of the matrix path agree bitwise; |L_t(j w_c)| = 1 within 1e-9. - Confirm the residuals: regulator ARE residual 4.441e-16 at the worked weights, filter ARE residual 4.441e-16 at q = 0 and 3.725e-09 at q* = 1e7, all below the 1e-8 max-entry gate. - Confirm every non-physical input raises ValueError: a < 0, q1 < 0, q2 < 0, r <= 0, w1 < 0, w2 <= 0, rv <= 0, q < 0, omega_min <= 0, omega_max <= omega_min, n_freq < 2, q_min <= 0, q_max < q_min, step <= 1, tol <= 0, and any non-finite parameter. - Run the contract test offline under both interpreters: python3 scripts/test_loop_transfer_recovery.py (31 tests, deterministic, stdlib only). ## Related leaves - gnc-autonomy/optimal-control/lqg-design: the compensator assembly that consumes this leaf's recovered estimator gain L(q*) as its filter gain input; its Pitfalls section declares loop-gain shaping toward full-state recovery unimplemented, the gap this leaf closes. - gnc-autonomy/optimal-control/lqr-design: owns the regulator gain design trade; the K used here is the fixed full-state target ingredient, with no weighting trade product. - gnc-autonomy/control/frequency-response-design: measures the margins of polynomial open loop transfer functions; this leaf evaluates loop transfer samples only to compare loop shapes and reports no margin numbers. - gnc-autonomy/navigation/kalman-filter-design: discrete-time scalar filter runs over sampled measurement streams; the recovery loop here is continuous-time over the two-state family. - gnc-autonomy/optimal-control/bang-bang-control, model-predictive-control, ilqr-ddp and dymos-trajectory: the time-domain and transcription alternatives; recovery runs no iteration or trajectory optimization. ## Pitfalls - Judging the match by margin numbers: the LTR loop-shape comparison is the grid-max complex mismatch M(q), never a gain or phase margin of the recovered loop; margin measurement is the adjacent control leaf's product. - Reporting the nominal loop as the deliverable: q = 0 (the lqg-design Example A filter) leaves the output loop up to 79% of the target peak off the full-state loop; only the recovered q* loop meets the tolerance gate. - Forgetting that the inflation is quadratic: the fictitious process noise grows as q^2, so l2(q) = sqrt(w2 + q^2) and the estimator roots sit at magnitude (w2 + q^2)^(1/4); sweeping q linearly moves the filter bandwidth by the square root only. - Expecting full recovery on non-minimum-phase plants: the canonical family is SISO and minimum phase, so M(q) -> 0 as q -> infinity; a plant with right-half-plane zeros or a multivariable input breaks the guarantee, and input-side recovery is out of scope. - Assuming the module designs the regulator or assembles the compensator: recovery_result returns the recovery verdict and the recovered filter gain, not a gain-design product with a weighting trade, and not a compensator realization or a separation verdict, which the lqg-design sibling owns. - Accepting negative weights or nonpositive covariances; the shared validation raises ValueError on every non-physical input, including the w2 = 0 case that leaves the double integrator without a stabilizing filter. - Recomputing the nominal loop as the target: the target is the full-state loop K (sI - A)^-1 B, not the q = 0 output loop of the LQG structure. ## Behavior contract (gate 3) The grid construction, both Riccati solves, the target and recovered loop evaluations, the mismatch metrics and the recovery verdict are exercised by the gate 3 contract test: scripts/test_loop_transfer_recovery.py against scripts/loop_transfer_recovery_logic.py (stdlib unittest, offline, deterministic). Run: python3 scripts/test_loop_transfer_recovery.py The test covers the worked-scenario anchors within 1e-6 relative per entry, the grid log-spacing identity, the target and recovered loop matrix-versus-closed-form identities, the two SISO multiplication orders, the ARE residuals below 1e-8 at the worked weights, at q = 0 and at q* = 1e7, the recovery decay law, the damped-plant recovery through the bisection branch, the cross-sibling filter identity with the lqg-design Example B anchors within 1e-12, bitwise determinism on repeat runs, stdlib-only imports, and ValueError rejection of every non-physical input listed in Verification. ## Compliance - ARP4754A is proprietary (SAE); name and paraphrase only per standards-map.yaml, reference-only: true. - compliance: STANDARDS-REF, gated: false.