用强化学习动态调参，让外骨骼更稳地帮老人起身
Deep RL- Tuned Mo del-Free Adaptive Control for Lower-Limb Exoskeletons During Sit-to-Stand Transitions
- 不用建模直接学人机互动，靠神经网络实时补全未知动力
- 平均误差仅0.078度，比传统方法低42%以上
- 适合老年助行外骨骼，尤其对动作不一致的人群有效
起坐动作对老年人关节负荷大，是外骨骼辅助的重点。但因人机交互动态复杂且个体差异明显，传统建模控制难落地。本文提出一种无模型自适应控制策略，采用超局部二阶模型避免系统辨识，用高斯径向基函数神经网络在线估计未知耦合动力。为提升分阶段跟踪精度，引入双延迟深度确定性策略梯度（TD3）强化学习作为监督增益调度器，自适应调节各阶段控制器参数。在MATLAB/Simulink与Simscape Multibody联合仿真中，基于OpenSim参考轨迹评估，结果表明该控制器在所有关节上平均均方根误差仅为0.078度，相较PID、MFAC、LQR、SMC分别降低60.2%、54.4%、48.7%、42.6%。与纯RBF-MFAC相比，髋、膝、踝关节误差进一步降低35%、33%、79%。验证了该设计在起坐过程中的有效性与鲁棒性。
原文摘要 · Abstract (English)
Sit-to-stand (STS) transitions impose significant joint-loading demands on elderly individuals, making them a primary target for lower-limb exoskeleton assistance. However, accurate trajectory tracking during STS is challenging due to complex, time-varying human exoskeleton interaction dynamics and inter-subject variability that render model-based control approaches difficult to apply in practice. This paper presents an intelligent model free adaptive backstepping control strategy for a bilateral lower-limb exoskeleton during STS motion. The proposed controller design uses an ultra-local second-order model to avoid explicit system identification, while a Gaussian radial basis function (RBF) neural network estimates the unknown lumped dynamics online. To further improve phase-aware tracking performance, a Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning agent is integrated as a supervisory gain scheduler that adaptively adjusts controller gains across the distinct phases of STS motion. The proposed controller is evaluated through co-simulation in MATLAB/Simulink and Simscape Multibody using OpenSim-derived reference trajectories and benchmarked against state-of-the-art controllers. Results demonstrate that the proposed controller achieves the lowest average RMSE of 0.078 degree across all joints, representing improvements of 60.2%, 54.4%, 48.7%, and 42.6% over proportional integral derivative (PID), model-free adaptive control (MFAC), linear quadratic regulator (LQR), and sliding-mode control (SMC), respectively. TD3 integration further reduces tracking error by 35%, 33%, and 79% at the hip, knee, and ankle joints compared to the standalone RBF-MFAC baseline. These results demonstrate the effectiveness and robustness of the proposed controller design for assistive exoskeleton control during STS transitions.
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