基于自适应被动性的缆驱动并联机器人多姿态参数化的位姿跟踪控制
Adaptive Passivity-Based Pose Tracking Control of Cable-Driven Parallel Robots for Multiple Attitude Parameterizations
AI总结：
本文提出一种基于自适应被动性的位姿跟踪控制方法，适用于多种姿态参数化，通过结合前馈和反馈控制器，实现CDPR的鲁棒性和轨迹跟踪。
摘要：
所提出的控制方法使用基于自适应前馈的控制器，为CDPR建立被动的输入输出映射，该映射与线性时不变严格正实反馈控制器结合使用，以保证鲁棒闭环输入输出稳定性和渐近位姿轨迹跟踪，通过被动性定理实现。所提控制器的创新之处在于其适用于多种载荷姿态参数化，包括任何无约束姿态参数化、四元数或方向余弦矩阵（DCM）。通过数值模拟CDPR的刚性与柔性电缆，验证了所提控制器的性能和鲁棒性。结果表明，正确定义CDPR的位姿误差至关重要，当使用四元数和DCM时，以乘法方式执行；而使用无约束姿态参数（如欧拉角序列）时，则以特定的加法方式执行。
英文摘要：
The proposed control method uses an adaptive feedforward-based controller to establish a passive input-output mapping for the CDPR that is used alongside a linear time-invariant strictly positive real feedback controller to guarantee robust closed-loop input-output stability and asymptotic pose trajectory tracking via the passivity theorem. A novelty of the proposed controller is its formulation for use with a range of payload attitude parameterizations, including any unconstrained attitude parameterization, the quaternion, or the direction cosine matrix (DCM). The performance and robustness of the proposed controller is demonstrated through numerical simulations of a CDPR with rigid and flexible cables. The results demonstrate the importance of carefully defining the CDPR's pose error, which is performed in multiplicative fashion when using the quaternion and DCM, and in a specific additive fashion when using unconstrained attitude parameters (e.g., an Euler-angle sequence).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
