跨平台自适应调参，让机器人PID控制器更智能高效。
Cross-Platform Learnable Fuzzy Gain-Scheduled Proportional-Integral-Derivative Controller Tuning via Physics-Constrained Meta-Learning and Reinforcement Learning Adaptation
- 用模糊调度+元学习，实现多机器人平台统一初始化。
- 在线微调使高负载关节误差降低80.4%，参数不确定下提升19.2%。
- 适合需快速适配新机器人或复杂工况的工程部署场景。
PID类控制器因简单可解释仍被广泛用于机器人系统，但其参数调优耗时且难以跨平台迁移。模糊增益调度可实现在线调整，但其每关节缩放与结论参数依赖平台，难系统化调优。本文提出一种分层框架，用于跨平台学习型模糊增益调度PID（LF-PID）调参。控制器采用共享模糊隶属度划分以保持误差语义一致，同时学习每关节缩放与Takagi-Sugeno结论参数，实现在线增益调度。结合物理约束的虚拟机器人生成，元学习基于机器人物理特征提供跨平台初始化，轻量强化学习（RL）阶段则在动力学不匹配下进行部署特异性微调。从三个基础仿真平台出发，通过质量（±10%）、惯性（±15%）、摩擦（±20%）有界扰动生成232个物理有效训练变体。在两个不同系统（9-DOF串联机械臂、12-DOF四足机器人）上评估跨平台泛化能力，多种干扰场景下，RL微调在元初始化基础上进一步提升性能：高负载关节误差最大减少80.4%（12.36°降至2.42°），参数不确定性下改善19.2%。我们还发现优化天花板效应：当元初始化基线存在局部缺陷时，在线微调收益显著；若基线整体性能已强，则改进有限。
原文摘要 · Abstract (English)
Motivation and gap: PID-family controllers remain a pragmatic choice for many robotic systems due to their simplicity and interpretability, but tuning stable, high-performing gains is time-consuming and typically non-transferable across robot morphologies, payloads, and deployment conditions. Fuzzy gain scheduling can provide interpretable online adjustment, yet its per-joint scaling and consequent parameters are platform-dependent and difficult to tune systematically. Proposed approach: We propose a hierarchical framework for cross-platform tuning of a learnable fuzzy gain-scheduled PID (LF-PID). The controller uses shared fuzzy membership partitions to preserve common error semantics, while learning per-joint scaling and Takagi-Sugeno consequent parameters that schedule PID gains online. Combined with physics-constrained virtual robot synthesis, meta-learning provides cross-platform initialization from robot physical features, and a lightweight reinforcement learning (RL) stage performs deployment-specific refinement under dynamics mismatch. Starting from three base simulated platforms, we generate 232 physically valid training variants via bounded perturbations of mass (+/-10%), inertia (+/-15%), and friction (+/-20%). Results and insight: We evaluate cross-platform generalization on two distinct systems (a 9-DOF serial manipulator and a 12-DOF quadruped) under multiple disturbance scenarios. The RL adaptation stage improves tracking performance on top of the meta-initialized controller, with up to 80.4% error reduction in challenging high-load joints (12.36 degrees to 2.42 degrees) and 19.2% improvement under parameter uncertainty. We further identify an optimization ceiling effect: online refinement yields substantial gains when the meta-initialized baseline exhibits localized deficiencies, but provides limited improvement when baseline quality is already uniformly strong.
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