Passivity-based Robust Design of Proportional-Derivative Navigation Guidance Law
Bibliographic record
Abstract
This article proposes a robust augmented proportional navigation guidance (PNG) law synthesis based on the passivity approach and applied to missiles whose ∞ight control dy- namics are represented by a second-order model with bounded parametric uncertainties. Instrumental in designing the proposed guidance law is an appropriate subsystem decom- position that enables passivation by both feedback and feedforward control. L2 stability of the missile-target closed-loop system is then inferred by the application of the passivity theorem and the extension of the Kalman-Yakubovich-Popov lemma to linear-time varying systems, thus ensuring robust stability of the null miss distance when the maneuvering target acceleration is in L2. Numerical simulations demonstrate the efiectiveness of the proposed guidance law. proposed. Uncertainties in the target and in the ∞ight control system of the missile are taken into account in the guidance synthesis by means of a parameter adaptation scheme. The ∞ight control dynamics are modeled as a second-order state-space form to which is added exogenous disturbances representing approxi- mation errors due to the use of curve fltting techniques in the modeling of the aerodynamic coe-cients. The guidance law proposed in, 4 although proven efiective by means of numerical simulations that show reduced miss distances as compared with PNG and sliding mode guidance laws, is relatively complex, showing little physical insight. Building upon previous results obtained by the authors in, 5 we propose in this article a robust augmented proportional navigation guidance law for the interception of maneuvering targets with acceleration in L2. Our proposed law is labeled proportional-derivative navigation guidance or PDNG. The pursuer dynamics is modeled as a second-order linear uncertain system, with parametric uncertainties expressed in polytopic form, whereas the missile-target relative kinematics is represented by a double integrator mapping the missile-target relative lateral accelerations to the miss distance. The terminal guidance law is obtained by a passivation of the missile-target closed-loop dynamics, which is represented by a linear, parameter-uncertain, time-varying model. This model is expressed in a suitable feedback subsystem decomposition. The time dependence arises from the fact that the PNG law is equivalent to an output feedback whose gains are functions of the time to go. The robust strict passivity property is obtained by output feedback and robust feedforward control laws whose parameters are tuned by applying an extension of the Kalman-Yakubovich- Popov (KYP) lemma to linear-time varying systems 6 and leveraging previous results obtained by the authors
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".