Reference adjustment for a high-acceleration and high-precision platform via A-type of iterative learning control

Author(s):  
J. H. Wu ◽  
H Ding

This paper studies the repetitive motion control of a high-acceleration and high-precision platform driven by linear motors. The control scheme comprises an anticipatory iterative learning control (A-ILC) component and a cascaded control structure including an inner-loop velocity PI controller and an outer-loop position P controller. During the motion process, the cascaded controller remains invariant while the A-ILC adjusts the reference command cycle by cycle to achieve better performance. Experiments are carried out to validate the proposed control structure. The results confirm that the proposed control scheme can improve the system performance significantly in both low-speed trajectory tracking motions and fast point-to-point motion. In the experiments, P-type and D-type ILCs are also utilized to adjust the reference command. Compared with the A type, P-type ILC leads to larger tracking error bounds and D-type ILC lacks a fast convergence rate for low-speed motions, while for fast point-to-point motion these two types of ILC are unable to work well.

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 152
Author(s):  
Dongqi Ma ◽  
Hui Lin

To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for the system, we determine the next iterative learning interval and conduct real-time correction on the learning gain. For the learning interval, as the number of iterations increases, the actual interval that needs correction constantly shortens, accelerating the convergence speed. Second, we analyze the specific structure of the controller while applying reasonable assumptions pertaining to its condition. Using the λ-norm, we analyze and apply our mathematical knowledge to obtain a strict mathematical proof on the P-type iterative learning control and obtain the condition of convergence for the controller. Finally, we apply the proposed method for periodic ripple inhibition of the torque rotation speed of the permanent magnet synchronous motor and establish the system model; we use the periodic load torque to simulate the ripple torque of the synchronous motor. The simulation and experimental results indicate the effectiveness of the method.


2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.


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