Constraint-Aware Discrete-Time PID Gain Optimization for Robotic Joint Control Under Actuator Saturation

• URL: http://arxiv.org/abs/2601.18639v3
• Date: Thu, 05 Feb 2026 13:10:26 GMT
• Title: Constraint-Aware Discrete-Time PID Gain Optimization for Robotic Joint Control Under Actuator Saturation
• Abstract summary: Practical loops deviate from continuous-time theory due to discrete-time execution, actuator saturation, and small delays and measurement imperfections.<n>We present an implementation-aware analysis and tuning workflow for saturated discrete-time joint control.
• Score: 18.71390061417015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The precise regulation of rotary actuation is fundamental in autonomous robotics, yet practical PID loops deviate from continuous-time theory due to discrete-time execution, actuator saturation, and small delays and measurement imperfections. We present an implementation-aware analysis and tuning workflow for saturated discrete-time joint control. We (i) derive PI stability regions under Euler and exact zero-order-hold (ZOH) discretizations using the Jury criterion, (ii) evaluate a discrete back-calculation anti-windup realization under saturation-dominant regimes, and (iii) propose a hybrid-certified Bayesian optimization workflow that screens analytically unstable candidates and behaviorally unsafe transients while optimizing a robust IAE objective with soft penalties on overshoot and saturation duty. Baseline sweeps ($τ=1.0$~s, $Δt=0.01$~s, $u\in[-10,10]$) quantify rise/settle trends for P/PI/PID. Under a randomized model family emulating uncertainty, delay, noise, quantization, and tighter saturation, robustness-oriented tuning improves median IAE from $0.843$ to $0.430$ while keeping median overshoot below $2\%$. In simulation-only tuning, the certification screen rejects $11.6\%$ of randomly sampled gains within bounds before full robust evaluation, improving sample efficiency.

Related papers

• Distribution-Aware Robust Bilevel Optimization: Quantile-Guided Huber Updates in Two-Timescale Stochastic Approximation [16.106678611834102]
  Bilevel optimization (BLO) is fundamental to hierarchical decision-making but suffers from critical instability under heavy-tailed noise.<n>Existing techniques typically rely on magnitude checks, which fail to distinguish informative geometric signals from outliers.
  arXiv  Detail & Related papers  (2026-06-21T10:57:37Z)
• CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving [19.474428775260034]
  We propose CLEAR, a framework that combines ultra-fast generative planning with deep semantic reasoning.<n> CLEAR employs Drive-JEPA as the visual encoder and replaces the multi-step denoising chain with a single-step conditional drift in a VAE latent space.<n>On the NAVSIM v1 benchmark, CLEAR achieves a state-of-the-art PDMS of 93.7.
  arXiv  Detail & Related papers  (2026-06-04T14:32:10Z)
• Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models [56.67321805551389]
  Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators.<n>The sampler, controlling the exploration behavior and denoising dynamics, is thus part of the policy.<n>We propose a new sampler that balances effective exploration with stability.
  arXiv  Detail & Related papers  (2026-05-22T11:37:22Z)
• Structured Quantum Optimal Control under Bandwidth and Smoothness Constraints-An Inexact Proximal-ADMM Approach for Low-Complexity Pulse Synthesis [0.0]
  I study an inexact Proximal-ADMM framework that combines gate-infidelity with sparsity, total-variation regularization, explicit band-limit projection, and box constraints in a single loop.<n>It is benchmarked against GRAPE, standard Krotov, and L-BFGS-B on a single-qubit $X$ gate, a leakage-prone qutrit task, and a two-qubit entangling gate.<n>The contribution should still be read as a numerical framework for constrained pulse synthesis rather than as a finished route to immediately deployable high-fidelity gates
  arXiv  Detail & Related papers  (2026-03-13T14:27:40Z)
• One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies [18.743330791557522]
  One-Step Flow Policy (OFP) is a from-scratch self-distillation framework for high-fidelity, single-step action generation without a pre-trained teacher.<n> Evaluations across 56 diverse simulated manipulation tasks demonstrate that a one-step OFP achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2026-03-12T21:58:12Z)
• $V_{0.5}$: Generalist Value Model as a Prior for Sparse RL Rollouts [81.48669089692189]
  Generalist Value Models (such as $V_0.5$) achieve pre-trained value estimation by explicitly encoding model capabilities in-context.<n>We propose $V_0.5$, which adaptively fuses the baseline predicted by such value model with the empirical mean derived from sparse rollouts.<n>$V_0.5$ significantly outperforms GRPO and DAPO, achieving faster convergence and over some 10% performance improvement.
  arXiv  Detail & Related papers  (2026-03-11T14:57:41Z)
• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• Trajectory Consistency for One-Step Generation on Euler Mean Flows [24.038760671907024]
  We propose emphEuler Mean Flows (EMF), a flow-based generative framework for one-step and few-step generation.<n>EMF enforces long-range trajectory consistency with minimal sampling cost.
  arXiv  Detail & Related papers  (2026-01-31T04:32:32Z)
• MAESTROCUT: Dynamic, Noise-Adaptive, and Secure Quantum Circuit Cutting on Near-Term Hardware [0.0]
  MaestroCut tracks a variance proxy in real time, triggers re-cutting when accuracy degrades, and routes shots using topology-aware priors.<n>Tier-1 simulations show consistent variance contraction and reduced mean-squared error versus uniform and proportional baselines.<n>Tier-2 emulation with realistic queueing and noise demonstrates stable latency targets, high reliability, and 1% software overhead under stress scenarios.
  arXiv  Detail & Related papers  (2025-08-31T12:01:16Z)
• SADA: Stability-guided Adaptive Diffusion Acceleration [24.250318487331228]
  Diffusion models have achieved remarkable success in generative tasks but suffer from high computational costs.<n>Existing training-free acceleration strategies that reduce per-step computation cost, while effectively reducing sampling time, demonstrate low faithfulness.<n>We propose Stability-guided Adaptive Diffusion Acceleration (SADA), a novel paradigm that accelerates sampling of ODE-based generative models.
  arXiv  Detail & Related papers  (2025-07-23T02:15:45Z)
• Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling [73.5602474095954]
  We study the non-asymptotic performance of approximation schemes with delayed updates under Markovian sampling. Our theoretical findings shed light on the finite-time effects of delays for a broad class of algorithms.
  arXiv  Detail & Related papers  (2024-02-19T03:08:02Z)
• Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
  We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks. We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution. We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
  arXiv  Detail & Related papers  (2021-07-02T16:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.