Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general nonlinear dynamic systems, finding globally “best” trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix (FIM). A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the FIM by three orders of magnitude, compared with the initial trajectory. Experimental results show that this optimized trajectory translates to an order-of-magnitude improvement in the parameter estimate error in practice.
Dynamic Parameter Identification is a useful tool for developing and evaluating robot control strategies. However, a multi degree of freedom robot arm has many parameters, and the process of determining them is challenging. Much research has been done in this area and experimental methods have been applied on several robot arms. To our knowledge, there is currently no set of inertial parameters, either by modelling or by estimation, available for the CRS A460 A465 arm, a popular laboratory table top robot. In this paper we review and compare a number of methods for dynamic parameter identification and for generating trajectories suitable for estimating the identifiable dynamic parameters of a given robot. We then present a step by step process for dynamic parameter identification of a serial manipulator, and demonstrate this process by experimentally identifying the dynamic parameters of the CRS A460 robot. Utilizing the virtual work principle, this paper presents a method for the dynamic formulation of redundant and non-redundant parallel manipulators for dynamic parameter identification. In modeling, the selection of pivotal point and the computation of inertia force and moment about the pivotal point are more crucial. The selection principle of pivotal point and force transmission on a rigid body are studied. In order to validate the method, the linear form of dynamic models of a 3-DOF parallel manipulator with actuation redundancy and its corresponding non-redundant parallel manipulator are derived. Abstract In this paper, the dynamic parameters, both inertial and frictional, of a 3-DOF RPS parallel manipulator are identified considering two important issues: the physical feasibility of the identified inertial parameters and the use of nonlinear friction models in the identification process in order to model the friction phenomenon at robot joints. The dynamic model of the parallel manipulator is obtained starting from the Gibbs–Appell equations of motion along with the Gauss principle of Least Action, and these equations of motion are rewritten in a their linear form with respect to the inertial parameters of the mechanical system. At this point, in accordance with the friction model considered, either linear or nonlinear, two types of dynamic models are dealt with: the totally and the partially linear with respect to the parameters to be identified. In order to solve the identification problem when nonlinear friction models are included, a nonlinear constrained optimization problem will be formulated and solved, instead of the Least Square Method, which is valid only for linear identification problems. It must be mentioned that the above-mentioned optimization problem will include the physical feasibility of the identified parameters in its formulation. The proposed procedure will be verified against a virtual parallel manipulator and finally, experimental identification processes are carried out over an actual parallel manipulator and a comparison is made between the LSM and the optimization process in the case of linear friction models, and between the linear and nonlinear friction models in the optimization process. This paper deals with the experimental identification of the dynamic parameters of parallel machines. The dynamic parameters are estimated by using the weighted least squares solution of an over determined linear system obtained from the sampling of the dynamic model along a closed loop exciting trajectory. Experimental results are exhibited for the H4 robot, a fully parallel structure providing 3 degrees of freedom (DOF) in translation and 1 DOF in rotation. A comparative study is performed depending on the available measurements, i.e., different sensor locations (motor, end effector). The determination of the minimum set of inertial parameters of robots contributes to the reduction of the computational cost of the dynamic models and simplifies the identification of the inertial parameters. These parameters can be obtained from the classical inertial parameters by eliminating those that have no effect on the dynamic model and by regrouping some others. A direct method is presented for determining the minimum set of inertial parameters of serial robots. The method permits determination of most of the regrouped parameters by means of closed-form relations. >