Physical feasibility of robot base inertial parameter identification: A linear matrix inequality approach

Abstract

Identification of robot dynamics is a key issue in boosting the performance of model-based control techniques, having also a key role in realistic simulation. Robot dynamic parameters have physical meaning, hence parameter estimations must correspond to physically feasible values. Since it is only possible to identify linear combinations of parameters (the base parameters) the physical feasibility of such combinations cannot be directly asserted. In this paper we show that feasibility conditions define a convex set and can be written as a linear matrix inequality (LMI), suitable for semidefinite programming (SDP) techniques. We propose three methods based on LMI–SDP to deal with the feasibility of base parameters. The first method checks if a given base parameter estimation has physical meaning. The second method corrects infeasible estimations, finding the closest feasible base parameters. These two methods can be applied to existing regression techniques. The third method performs parameter identification through ordinary least squares regression constrained to the feasible space, guaranteeing the optimal solution. Experiments with a seven-link robot manipulator are provided, and efficiency and scalability are discussed.

Online payload identification for quadruped robots

“…First the trunk mass was estimated from contact forces using (7). For sake of brevity, numerical results are omitted.…”

“…However, the drawback of these methods is that they rely on the accuracy of the robot dynamic model [1], [6], whose parameters are usually calculated using computeraided design (CAD) software [1], [7]- [9]. Although CAD softwares can calculate these parameters accurately, additional elements like hoses and cables are usually not taken into account [1].…”

Cascade force control for autonomous beating heart motion compensation

“…WAM robot dynamic parameters have been identified and estimated through the methodology proposed in Sousa and Cortesão (2014) and are used to generate inverse dynamic model (IDM). Computed torque control in the task space is implemented to linearize the WAM robot.…”

A dynamically consistent hierarchical control architecture for robotic-assisted tele-echography with motion and contact dynamics driven by a 3D time-of-flight camera and a force sensor

“…With the derivation of the dynamic model [7], it is possible to achieve a linear and decoupled system through nonlinear feedback linearization. Let the total torques acting on the manipulator be defined by…”