Non-redundant inertial parameters determination for dynamic identification of branched articulated robots

Abstract

Purpose Branched articulated robots (BARs) are highly non-linear systems; accurate dynamic identification is critical for model-based control in high-speed and heavy-load applications. However, due to some dynamic parameters being redundant, dynamic models are singular, which increases the calculation amount and reduces the robustness of identification. This paper aims to propose a novel methodology for the dynamic analysis and redundant parameters elimination of BARs. Design/methodology/approach At first, the motion of a rigid body is divided into constraint-dependent and constraint-independent. The redundancy of inertial parameters is analyzed from physical constraints. Then, the redundant parameters are eliminated by mapping posterior links to their antecedents, which can be applied for re-deriving the Newton–Euler formulas. Finally, through parameter transformation, the presented dynamic model is non-singular and available for identification directly. Findings New formulas for redundant parameters elimination are explicit and computationally efficient. This unifies the redundant parameters elimination of prismatic and revolute joints for BARs, and it is also applicable to other types of joints containing constraints. The proposed approach is conducive to facilitating the modelling phase during the robot identification. Simulation studies are conducted to illustrate the effectiveness of the proposed redundant parameters elimination and non-singular dynamic model determination. Experimental studies are carried out to verify the result of the identification algorithm. Originality/value This work proposes to determine and directly identify the non-redundant dynamic model of robots, which can help to reduce the procedure of obtaining the reversible regression matrix for identification.