Iterative Identification of Robot Dynamic Parameters Based on Logistic Function

The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.

Iterative identification and control design with optimal excitation signals based on v-gap

An iterative identification and control design method based on v-gap is given to ensure the stability of closed-loop system and control performance improvement. The whole iterative procedure includes three parts： the optimal excitation signals design, the uncertainty model set identification and the stable controller design. Firstly the worst case v-gap is used as the criterion of the optimal excitation signals design, and the design is performed via the power spectrum optimization. And then, an uncertainty model set is attained by system identification on the basis of the measure signals. The controller is designed to ensure the stability of closed-loop system and the closed-loop performance improvement. Simulation result shows that the proposed method has good convergence and closed-loop control performance.