CAD.5:11 - SoTA-Echoing

The precise question here is how to choose realizable observations and sufficient internal memory for bounded-error sampled control. The method joins state estimation and robust control constructions through their information, timing and admissibility conditions.

• Nakahira, Liu, Sejnowski and Doyle, 2021, “Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control” supplies a historical model of complementary pathways. Retain its demand for a whole-task and resource comparison; do not universalize its particular speed–accuracy relation or infer IFP from that DeSS model.
• “Internal Feedback in Biological Control: Architectures of Diversity and Delays,” 2022 version supplies a related mathematical contrast between fast partial and delayed observations. Its selected-model and ablation comparisons do not establish necessity across all retuned architectures. Sections :4.6 and :5.4 retain that distinction.
• Li, Sarma, Sejnowski and Doyle, 2023, “Internal feedback in the cortical perception–action loop enables fast and accurate behavior”, especially its estimation and attention constructions, makes the role of internally available actions and predictions explicit. Adapt that mechanism, with its stated model limits. Do not adopt its asserted scalar no-IFP optimum: in the one-tick-delay, unit-white-noise model of pp.3/11 at (a=1), the proposed gain (k_1=-1/4) yields stationary state variance (80/27), while (k_1=-1/2) yields (12/5); both closed loops are stable. This comparison uses the article’s state-only cost, not a control-effort penalty. The guarantees here use the separately stated bounded-disturbance criterion.
• Casini, Garulli and Vicino, “Adaptive Threshold Selection for Set Membership State Estimation with Quantized Measurements,” author copy linked to the 2025 publication, §§3–6, develops feasible-set updates and adaptive quantization (IEEE Transactions on Automatic Control). Adopt the set interpretation and explicit outer-approximation cost. Its higher-dimensional threshold choice targets output uncertainty, so control constraints and heterogeneous delivery timing still need the construction here. In particular, output uncertainty and its asymptotic boundedness are not the same requirement as the all-time state and input constraints in :4.6.
• Dutta, Wilde and Smith, 2023 version, “A Unified Approach to Optimally Solving Sensor Scheduling and Sensor Selection Problems in Kalman Filtering”, §§II–IV, is a serious alternative when the selected criterion is weighted expected estimation or LQG cost under finite-horizon resource constraints. Its mixed-integer construction can compare schedules under that stochastic formulation. Do not relabel its covariance result as a hard feasible-state bound; use it when its criterion is the required one.

Choose between these approaches by the required conclusion, permitted uncertainty model and executable cost. A fixed observer or current complete observation can remain preferable. Reopen the construction when the demanded constraint, admissible timing, sensor model, available computation or a relevant stronger result changes the design conclusion; a later publication date alone does not establish dominance.