基于库伦-粘性摩擦模型的大腿假肢动力学参数辨识

基于模型的智能假肢控制方式具有物理意义明确、参数变量少的优势,但受建模误差、模型不确定等因素的影响,其控制精度仍有待进一步提高,而有效措施之一便是假肢先验动力学建模与辨识.本文针对实验室新设计的动力大腿假肢,研究了基于库伦-粘性摩擦的大腿假肢动力学参数辨识问题.首先,基于拉格朗日方法建立了具有固定传动比动力膝关节和非线性传动比动力踝关节的大腿假肢动力学模型；其次,采用库伦-粘性摩擦模型来描述假肢动力学模型中的关节摩擦行为；最后,通过粒子群优化算法辨识了大腿假肢的动力学参数.结果表明,相比于基于3D建模软件的估计参数,基于辨识参数重构的膝、踝关节电机扭矩与实测扭矩的均方根误差分别降低了93.55%和80.83%,模型精度得到了显著提高.这一结果不仅验证了本文假肢动力学建模与参数辨识方法的有效性,也为假肢后续的高精度控制提供了技术支撑.

Dynamic Parameter Identification for an Ankle-Knee Prosthesis with Coulomb-Viscous Friction

Model-based prosthetic control strategies are still incompetent for practical implementations because modeling uncertainties and errors limit their precision. This paper investigates identifying the dynamic parameters of ankle-knee prostheses based on Coulomb-viscous friction for a newly designed powered prosthesis in the laboratory. Firstly, a powered ankle-knee prosthesis dynamic model, considering the fixed transmission ratio for the knee joint and the nonlinear transmission ratio for the ankle joint, is developed based on the Lagrange method. Secondly, a Coulomb-viscous friction model is employed to describe the joint friction characteristics in the prosthesis dynamic model. Finally, the dynamic parameters of the powered prosthesis are identified by the particle swarm optimization algorithm. Compared with CAD estimation, the root-mean-square errors between the actual and the reconstructed torques after parameter identification are reduced by 99.07% for the knee joint and 83.33% for the ankle joint, indicating that the model precision has been significantly enhanced. The effectiveness, as a consequence, provides a solid technical foundation for accurate prosthesis control.