论文标题
肌肉骨骼动力学的运动优化：一种基于平坦的多项式方法
Motion Optimization for Musculoskeletal Dynamics: A Flatness-Based Polynomial Approach
论文作者
论文摘要
引入了一种新的肌肉骨骼动力学模型轨迹优化的方法。该模型将描述的刚体和肌肉动力学与由神经控制输入驱动的山型模型相结合。 The objective is to find input and state trajectories which are optimal with respect to a minimum-effort objective and meet constraints associated with musculoskeletal models.努力的量度是由激动剂 - 抗抗激肌的成对平均力的积分给出的。 The concepts of flat parameterization of nonlinear systems and sum-of-squares optimization are combined to yield a method that eliminates the numerous set of dynamic constraints present in collocation methods. With terminal equilibrium, optimization reduces to a feasible linear program, and a recursive feasibility proof is given for more general polynomial optimization cases.本文的方法可以用作用于层次和后水解控制方案的快速有效求解器的基础。包括两个模拟示例以说明所提出的方法
A new approach for trajectory optimization of musculoskeletal dynamic models is introduced. The model combines rigid body and muscle dynamics described with a Hill-type model driven by neural control inputs. The objective is to find input and state trajectories which are optimal with respect to a minimum-effort objective and meet constraints associated with musculoskeletal models. The measure of effort is given by the integral of pairwise average forces of the agonist-antagonist muscles. The concepts of flat parameterization of nonlinear systems and sum-of-squares optimization are combined to yield a method that eliminates the numerous set of dynamic constraints present in collocation methods. With terminal equilibrium, optimization reduces to a feasible linear program, and a recursive feasibility proof is given for more general polynomial optimization cases. The methods of the paper can be used as a basis for fast and efficient solvers for hierarchical and receding-horizon control schemes. Two simulation examples are included to illustrate the proposed methods
