On the Stability of Linear Active Disturbance Rejection Control

Error model-oriented vibration suppression control of free-floating space robot with flexible joints based on adaptive neural network

To meet the practical operation requirements of manipulators in industry, service and collaboration scenarios, trajectory tracking accuracy and system stability of manipulators are usually regarded as critical control objectives. This paper proposes a novel manipulator control method with three-level constraints, called the real deep Lagrangian network (Real-DeLaN). In this method, real data (also called physical data) are collected from the physical manipulator and utilized to train the Lagrangian dynamics network model to improve the migration ability from simulation to reality. The torque output of the dynamic model is corrected by the real-time calculation of friction in the network. The contact force on the end effector of the manipulator is compensated based on the principle of virtual displacement work. The experimental results show that Real-DeLaN can better control the joints to perform trajectory tracking, reduce the friction error of the manipulator, and show better anti-interference ability. 

Dynamics Modeling with Realistic Constraints for Trajectory Tracking Control of Manipulator

Data-driven Multiplayer Mixed-zero-sum Game Control of Modular Robot Manipulators with Uncertain Disturbance

In this paper, the stability and smoothness of trajectory planning and attitude control of the manipulator are studied. The DH motion model of Kinova Jaco Gen-2 manipulator was established to solve the forward and inverse kinematics equations. Firstly, the third-order Bezier curve is designed to plan the trajectory of manipulator, which has good continuity and smoothness, and can control the workspace. Secondly, an attitude interpolation method based on quaternion is proposed. And different attitude interpolation methods are compared and analyzed, and spherical linear interpolation method is used to solve the problem of uneven velocity of the linear interpolation method. Finally, MATLAB robot toolbox is used to simulate and verify that the angle of each joint changes smoothly, and the trajectory of manipulator has good smoothness and stability based on Bezier curve and quaternion interpolation. According to the experimental results, the path planning based on the third order Bezier curve and spherical linear interpolation has a continuous smooth joint trajectory curve and a continuous joint angular velocity curve. The manipulator can move smoothly to the target point under the constraint of the end posture. Therefore, the trajectory planning method can be used as an ideal planning method for manipulator.

Trajectory Planning and Attitude Control of Manipulator

To solve the problems of model uncertainties, dynamic coupling, and external disturbances, a modified linear active disturbance rejection controller (MLADRC) is proposed for the trajectory tracking control of robot manipulators. In the computer simulation, MLADRC is compared to the proportional-derivative (PD) controller and the regular linear active disturbance rejection controller (LADRC) for performance tests. Multiple uncertain factors such as friction, parameter perturbation, and external disturbance are sequentially added to the system to simulate an actual robot manipulator system. Besides, a two-degree-of-freedom (2-DOF) manipulator is constructed to verify the control performance of the MLADRC. Compared with the regular LADRC, MLADRC is significantly characterized by the addition of feedforward control of reference angular acceleration, which helps robot manipulators keep up with target trajectories more accurately. The simulation and experimental results demonstrate the superiority of the MLADRC over the regular LADRC for the trajectory tracking control.

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Haikuo Shen

Papers

Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking