Moh Kamalul Wafi: Deep Reinforcement Learning with Smith-Predictor Delay Compensation

Moh Kamalul Wafi: Deep Reinforcement Learning with Smith-Predictor Delay Compensation

Moh Kamalul Wafi studies whether a Smith predictor can help deep reinforcement learning controllers cope with constant input delays. The approach trains a deep deterministic policy gradient (DDPG) actor in a nominal delay-free environment, then deploys it with a Smith predictor set to a fixed nominal delay. For linear controllers, the paper derives closed-loop maps when the plant or delay does not match the nominal model, and gives small-gain and $\nu$-gap robustness conditions. It also presents a separate delay-independent Razumikhin stability condition for a fixed Lipschitz state-feedback policy. For delay identification, Wafi studies output-error estimates from a fixed input–output record, but explicitly says those results do not imply stability of an online adaptive predictor. Numerical experiments compare PID, LQR and DDPG on stable and open-loop unstable first-order plants with valve stiction. In those tested configurations, the results illustrate a benefit from predictor compensation and sensitivity of standalone DDPG to delay and plant changes. The abstract does not establish that the observed benefit extends beyond those configurations.

Publication: b57cadce-5b51-43c6-8fdf-3fffb95235a8

attribution — pass: The paper is correctly attributed to its sole listed author, Moh Kamalul Wafi, on arXiv eess.SY. The summary's 'Wafi studies' and 'explicitly says' framing keeps the paper's claims attributed to the author.

Authors:Moh Kamalul Wafi

summary — uncertain: The summary is faithful but hard for a non-specialist to follow. It never says why delays matter, although the abstract does: they can degrade feedback and destabilize a loop. Central terms are used without plain-language context: Smith predictor, plant, constant input delays, PID, LQR, and valve stiction. Raw LaTeX '$\nu$-gap' appears in the text. Smallest repair: open with the abstract's motivation, gloss the Smith predictor briefly or via a tooltip, and add tooltips for the other terms. Optionally, note this is an extended version of an ACC conference paper, with experiments retained from it.

Time delays can degrade feedback performance and destabilize a closed loop that is stable without delay.

date — pass: The originalPublished date of 2026-10-31 matches the v1 submission date. The feed date of Nov 3 is later and was correctly not used.

[v1] Sat, 31 Oct 2026 06:06:51 UTC (1,275 KB)

support — pass: Each claim matches the abstract verbatim in substance, and the scope limits are preserved: the tested configurations only, and identification not implying online stability. The closing caveat and the question follow from the source's own hedge and add no unsupported benefit or forecast.

The results illustrate the benefit of predictor compensation in the tested configurations and the sensitivity of standalone DDPG to delay and plant changes.

media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source.