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Estimating symmetric, positive definite matrices in robotic control

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2 Author(s)
Yixin Chen ; Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA ; McInroy, J.E.

In a number of contexts relevant to control problems, including estimation of robot dynamics, covariance, and smart structure mass and stiffness matrices, we need to solve an over-determined set of linear equations AX ≈ B with the constraint that the matrix X be symmetric and positive definite. In the classical least squares method, the measurements of A are assumed to be free of error. Hence, all errors are confined to B. Thus, the "optimal" solution is given by minimizing ||AX - B||F2. However, this assumption is often impractical. Sampling errors, modeling errors, and, sometimes, human errors bring inaccuracies to A as well. We introduce a different optimization criterion, based on area, which takes the errors in both A and B into consideration. The analytic expression of the global optimizer is derived. The algorithm is applied to identify the joint space mass-inertia matrix of a Gough-Stewart platform. Experimental results indicate that the new approach is practical, and improves performance.

Published in:

Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on  (Volume:4 )

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