Robust multi-objective optimization of parallel manipulators

This paper presents a novel robust optimal design for parallel manipulators to optimize the performance indices subject to the unavoidable effect of the uncertainties. The robust optimization proposed in the present contribution consists of a multi-objective optimization problem that aims at maximizing the performance index and robustness criterion simultaneously. The design variables should be adjusted to minimize the effects of the uncertainties and maximize the performance index. The single-objective optimization problem is also carried out to evaluate the optimal design obtained by using the proposed robust optimization approach. Numerical results illustrate the benefits of the proposed robust optimization applied to the optimal kinematic design of a parallel Cartesian manipulator with clearances and the optimal dynamic design of a Stewart-Gough platform.

Automated summary

This paper presents a novel robust optimal design for parallel manipulators to optimize performance indices while considering the effects of uncertainties. The proposed robust optimization includes a multi-objective approach and adjustment of design variables to minimize uncertainty effects and maximize performance indices. The benefits of this approach are illustrated through numerical results on the optimal kinematic design of a parallel Cartesian manipulator and the optimal dynamic design of a Stewart-Gough platform.