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 without hardware experiments.

翻译：旋转驱动的精确调节是自主机器人技术的基础，然而实际PID回路因离散时间执行、执行器饱和以及微小延迟和测量缺陷而偏离连续时间理论。本文提出了一种面向饱和离散时间关节控制的实现感知分析与整定工作流。我们（i）利用朱里判据推导了欧拉和精确零阶保持（ZOH）离散化下的PI稳定性区域，（ii）评估了饱和主导工况下的离散反向计算抗饱和实现方案，以及（iii）提出了一种混合认证贝叶斯优化工作流，该流程在优化鲁棒IAE目标（对超调和饱和占空比施加软惩罚）的同时，筛选解析不稳定候选参数和行为不安全暂态过程。基线参数扫描（$τ=1.0$~s，$Δt=0.01$~s，$u\in[-10,10]$）量化了P/PI/PID控制的上升/稳定趋势。在模拟不确定性、延迟、噪声、量化及更严格饱和的随机化模型族下，鲁棒导向整定将IAE中位数从$0.843$提升至$0.430$，同时保持超调中位数低于$2\%$。在纯仿真整定中，认证筛选机制在完整鲁棒性评估前即拒绝边界内$11.6\%$的随机采样增益，从而在不依赖硬件实验的情况下提升了采样效率。