State Attraction Control of Cable-Driven Soft Robot with Human-inspired Decision-Making Integration
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摘要: 绳驱动软体机器人通过柔性本体与绳传动相结合, 在结构轻量化、运动柔顺性及人机交互安全性等方面具有显著优势, 在协作操作与安全交互场景中展现出重要应用潜力. 然而, 软体材料固有的强非线性与大变形特性, 以及绳驱动结构引入的多自由度耦合和参数不确定性, 使其在复杂环境下的稳定控制与抗扰性能面临较大挑战. 针对上述问题, 构建一种仿生绳驱动软体机器人原型系统, 建立了相应的运动学与动力学模型, 并提出了一种融合仿人决策的状态吸引控制方法. 该方法引入脉冲神经网络以模拟人类决策行为, 实现对绳驱动软体机器人启停策略与期望轨迹的自适应更新; 设计了一种融合状态吸引函数与仿人决策的鲁棒轨迹跟踪控制策略, 用以约束跟踪误差在模型不确定性与外部扰动条件下的收敛方向, 并基于Lyapunov理论证明了闭环系统的稳定性. 仿真与实物实验结果表明, 所提出的方法在鲁棒性、人机交互安全性及动态响应性能等方面具有良好表现, 验证了其在实际人机交互场景中的可行性与有效性.Abstract: Cable-driven soft robot arms (CDSRAs) combine a compliant body with cable transmission, offering notable advantages in lightweight design, motion compliance, and human-robot interaction safety, thus showing great potential in collaborative manipulation and interaction scenarios. However, the strong nonlinearity and large deformation of soft materials, together with the multi-degree-of-freedom coupling and parameter uncertainties introduced by cable-driven structures, pose challenges to stable control and disturbance rejection in complex environments. To address these issues, this paper develops a human-inspired cable-driven soft robot prototype, establishes its kinematic and dynamic models, and proposes a state-attracted control framework for CDSRAs incorporating human-inspired decision (HID). In this framework, a spiking neural network (SNN) is introduced to emulate human decision-making, enabling adaptive updates of the start-stop strategy and desired trajectories. Moreover, a robust trajectory-tracking controller integrating a state-attracted function (SAF) with HID is designed to constrain tracking-error convergence under model uncertainties and external disturbances, while closed-loop stability is proved via Lyapunov theory. Simulation and experimental results demonstrate that the proposed method achieves favorable performance in robustness, human-robot interaction safety, and dynamic response, validating its feasibility and effectiveness in practical human-robot interaction scenarios.
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图 6 融合状态吸引函数与仿人决策控制策略的总体框架
Fig. 6 General framework of the integrated SAF-HID control strategy
图 7 SAF-HID控制在交互与扰动条件下的性能对比
Fig. 7 Performance comparison of SAF-HID control under interaction and disturbance conditions
图 9 外部扰动作用下不同控制方法的跟踪误差速度
Fig. 9 Tracking error rates under different control methods with external disturbances
图 8 外部扰动条件下不同控制方法的跟踪误差
Fig. 8 Tracking errors under different control methods with external disturbances
图 10 基于SAF-HID控制的CDSRA的动态安全避障实验
Fig. 10 Dynamic safe obstacle avoidance of CDSRA under SAF-HID control
图 11 不同控制方法在关节空间中的避障轨迹
Fig. 11 Joint-space obstacle avoidance trajectories under different control methods
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