Joint parameter and state estimation for regularized time-discrete multibody dynamics

TL;DR

This paper presents a fast, robust method for offline joint parameter and state estimation in multibody dynamics, effectively handling unobserved degrees of freedom and frictional constraints using nonlinear least squares optimization.

Contribution

It introduces a novel approach combining inverse dynamics and observation errors with automatic differentiation for efficient parameter and state estimation in multibody systems.

Findings

Fast convergence within seconds

Good accuracy across multiple data sets

Robustness to method parameter choices

Abstract

We develop a method for offline parameter estimation of discrete multibody dynamics with regularized and frictional kinematic constraints. This setting leads to unobserved degrees of freedom, which we handle using joint state and parameter estimation. Our method finds the states and parameters as the solution to a nonlinear least squares optimization problem based on the inverse dynamics and the observation error. The solution is found using a Levenberg-Marquardt algorithm with derivatives from automatic differentiation and custom differentiation rules for the complementary conditions that appear due to dry frictional constraints. We reduce the number of method parameters to the choice of the time-step, regularization coefficients, and a parameter that controls the relative weighting of inverse dynamics and observation errors. We evaluate the method using synthetic and real measured data, focusing on performance and sensitivity to method parameters. In particular, we optimize over a 13-dimensional parameter space, including inertial, frictional, tilt, and motor parameters, using data from a real Furuta pendulum. Results show fast convergence, in the order of seconds, and good agreement for different time-series of recorded data over multiple method parameter choices. However, very stiff constraints may cause difficulties in solving the optimization problem. We conclude that our method can be very fast and has method parameters that are robust and easy to set in the tested scenarios.