2020/08/12 by Hanz Richter, Richter, Hanz, Holly Warner +1
Engineering · Neuroscience · #FOS: Electrical engineering #FOS: Mathematics #Motor Control and Adaptation #Muscle activation and electromyography studies #Optimization and Control (math.OC) #Prosthetics and Rehabilitation Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.05318
openalex publication_date 2020/08/12 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
A new approach for trajectory optimization of musculoskeletal dynamic models\nis introduced. The model combines rigid body and muscle dynamics described with\na Hill-type model driven by neural control inputs. The objective is to find\ninput and state trajectories which are optimal with respect to a minimum-effort\nobjective and meet constraints associated with musculoskeletal models. The\nmeasure of effort is given by the integral of pairwise average forces of the\nagonist-antagonist muscles. The concepts of flat parameterization of nonlinear\nsystems and sum-of-squares optimization are combined to yield a method that\neliminates the numerous set of dynamic constraints present in collocation\nmethods. With terminal equilibrium, optimization reduces to a feasible linear\nprogram, and a recursive feasibility proof is given for more general polynomial\noptimization cases. The methods of the paper can be used as a basis for fast\nand efficient solvers for hierarchical and receding-horizon control schemes.\nTwo simulation examples are included to illustrate the proposed methods\n