Parameter Identification for Multibody Dynamic Systems

This paper presents a parameter identification technique for multibody dynamic systems, based on a nonlinear least–square optimization procedure. The procedure identifies unknown parameters in the differential–algebraic multibody system model by matching the acceleration time history of a point of interest with given data. Derivative information for the optimization process is obtained through dynamic sensitivity analysis. Direct differentiation methods are used to perform the sensitivity analysis. Examples of the procedure are presented, applying the technique both to perfect data; i.e. data produced by the assumed model with the optimal choice of parameters, and to experimental data; i.e. data measured on the real system and thus subject to noise and modelling imperfections. INTRODUCTION The objective of parameter estimation is to fit a given, fixed mathematical model, , to experimentally measured data. A clear distinction should be drawn between the problem of parameter estimation and the problem of system identification. When performing system identification, one has the freedom to select both the model which describes the physical phenomena and the model parameters which minimize the differences between the model and the data. When performing parameter estimation, the model is predetermined whereas the parameters are the only free variables which can be used to minimize differences between the model and the data. The goal of this work is to apply the general theory of parameter identification to the particular type of multibody system models, taking advantage of their special structure. ANALYSIS The multibody dynamic systems under consideration involve constrained rigid body motion under the action of time varying loads. The differential– algebraic equations of motion for these systems can be expressed in the form M(q)q .. T q(q) Q(t, q, q . ) (q) 0 (1)