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.

AN ACCELERATION FOR THE EIGENSYSTEM REALIZATION ALGORITHM WITH PARTIAL SINGULAR VALUES DECOMPOSITION

The real-time identification of dynamic parameters is importantfor the control system of spacecraft. The eigensystme realizationalgorithm (ERA) is currently the typical method for such applica-tion. In order to identify the dynamic parameter of spacecraftrapidly and accurately, an accelerated ERA with a partial singularvalues decomposition (PSVD) algorithm is presented. In the PSVD, theHankel matrix is reduced to dual diagonal form first, and thentransformed into a tridiagonal matrix.

Study on Control Technology of Tendon Bionic Driving Robot System

Although traditional position-controlled industrial robots can be competent for most assembly tasks,they cannot complete complex tasks that frequently interact with the external environment.The current research on exoskeleton robots also has problems such as excessive inertia of exoskeleton robots,poor system integration and difficult human–computer interaction control.To solve these problems,this paper independently develops a tendon driving robotic system composed of a tendon driving robotic arm and an upper limb exoskeleton,and studies its control technology.First,the robot system is selected,configured,and constructed.Second,the kinematics of the robot is analyzed,and then the dynamics are studied,and the parameter identification experiment of single degree of freedom is completed.Finally,the research on zero-force control and impedance control of the robot has effectively improved the robot’s human–machine integration ability,ensured the flexibility and compliance in the process of human–computer interaction.The compliant control problem expands the usage scenarios and application scope of robots and contributes to the realization of complex operations of this group of robots in unstructured environments.