基于残差深度强化学习的缆绳驱动下肢康复机器人计算力矩控制（针对扰动与参数不确定性场景）
Residual Deep Reinforcement Learning-Based Computed Torque Control for a Cable-Driven Lower-Limb Rehabilitation Robot under Disturbances and Parametric Uncertainties
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中文总结 AI 辅助
针对缆绳驱动下肢康复机器人的轨迹跟踪难题，提出残差深度强化学习增强的计算力矩控制框架，仿真显示其可提升跟踪与扰动抑制能力，保留可解释性且具鲁棒性。
中文摘要 AI 辅助
缆绳驱动下肢康复机器人的精确轨迹跟踪极具挑战性，因为模型不确定性、外部扰动、关节约束以及仅能拉力驱动的缆绳特性会降低标称控制性能。传统基于模型的控制器具备可解释的控制结构，但对模型失配仍较为敏感；而完全基于学习的控制则会降低透明度，使感知约束的操作复杂化。本研究提出一种残差深度强化学习增强的计算力矩控制框架，其中计算力矩控制生成标称指令，有界深度确定性策略梯度（Deep Deterministic Policy Gradient，DDPG）策略仅提供额外的补偿力矩。该方法在标称、不确定、扰动、组合及泛化条件下进行仿真评估，同时开展轨迹跟踪、关节极限、缆绳需求、工作空间可行性及缆绳雅可比矩阵诊断。在所有评估条件下，残差控制器相较于计算力矩控制，提升了跟踪性能与扰动抑制能力，同时保留了可解释的基于模型指令结构，并在代表性评估中满足了报告的可行性检查。更广泛的测试表明，跟踪性能的提升可在代表性案例之外持续存在，同时也暴露出依赖轨迹的约束限制。这些结果支持有界残差学习作为基于仿真的康复机器人控制的实用鲁棒性增强策略，并为进一步的感知约束及实验验证提供了动力。
英文摘要
Accurate trajectory tracking in cable-driven lower-limb rehabilitation robots is challenging because model uncertainty, external disturbances, joint constraints, and pull-only cable actuation can degrade nominal control performance. Conventional model-based controllers provide an interpretable control structure but remain sensitive to model mismatch, whereas fully learning-based control can reduce transparency and complicate constraint-aware operation. This study proposes a residual deep reinforcement learning-enhanced computed torque control framework in which computed torque control generates the nominal command and a bounded Deep Deterministic Policy Gradient policy supplies only an additional compensating torque. The approach is evaluated in simulation under nominal, uncertain, disturbed, combined, and generalization conditions, together with trajectory-tracking, joint-limit, cable-demand, workspace-feasibility, and cable-Jacobian diagnostics. Across the evaluated conditions, the residual controller improves tracking and disturbance rejection relative to computed torque control while preserving the interpretable model-based command structure and satisfying the reported feasibility checks in the representative evaluation. Broader tests indicate that tracking improvements can persist beyond the representative case while also exposing trajectory-dependent constraint limitations. These results support bounded residual learning as a practical robustness-enhancement strategy for simulation-based rehabilitation robot control and motivate further constraint-aware and experimental validation.
发表机构
- Kharazmi University（哈拉兹米大学）
机构由 AI 辅助整理，请以论文原文为准。
