A Sparse Optimization-Based Control Method for Manipulator With Simultaneous Potential Energy Minimization

Manipulators may often endure large-scale potential energy variations during kinematic control, producing unsafe oscillations of posture-hold effects. In this brief, for the first time, a sparse-optimization-based control method is proposed to simultaneously guarantee task accuracy and reduce potential energy variations for manipulators. The proposed control approach is formulated as an
$L_{p}$
-norm sparse optimization paradigm with the variation of the potential energy as the constraint. Simulation results on the
$n$
-link planar manipulator and experiment results on a 6-DoF manipulator system verify the proposed control method for accurate motion control tasks with potential energy minimization, with the average potential energy variations of the manipulators largely diminished with the sparse kinematic resolutions.