Disturbance observer based iterative learning control method for a class of systems subject to mismatched disturbances

2016 ◽  
Vol 39 (11) ◽  
pp. 1749-1760 ◽  
Author(s):  
Jiankun Sun ◽  
Shihua Li
Keyword(s):  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Simulation Analysis ◽  
Permanent Magnet Synchronous Motor ◽  
Learning Control ◽  
Iterative Learning ◽  
Closed Loop System ◽  
Practical Engineering ◽  
Mismatched Disturbances

This paper develops a systematic iterative learning control (ILC) strategy for systems with mismatched disturbances. The systems with mismatched disturbances are more general and widely exist in practical engineering, where the standard disturbance observer based ILC method is no longer available. To this end, this note proposes a novel ILC scheme based on the disturbance observer, which consists of two parts: a baseline ILC term for stabilizing the nominal system and a disturbance compensation term for attenuating mismatched disturbances by choosing an appropriate compensation gain. It is proven that the performance of the closed-loop system is effectively improved. Finally, the simulation analysis for a permanent-magnet synchronous motor servo system demonstrates the feasibility and efficacy of the proposed method.

scholarly journals Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 152
Author(s):  
Dongqi Ma ◽  
Hui Lin
Keyword(s):  
Permanent Magnet ◽  
Iterative Learning Control ◽  
Mathematical Proof ◽  
Permanent Magnet Synchronous Motor ◽  
Learning Control ◽  
Synchronous Motor ◽  
Iterative Learning ◽  
Learning Gain ◽  
Periodic Load ◽  
P Type

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.

scholarly journals A New Linear Motor Force Ripple Compensation Method Based on Inverse Model Iterative Learning and Robust Disturbance Observer

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Xuewei Fu ◽  
Xiaofeng Yang ◽  
Zhenyu Chen
Keyword(s):  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Learning Control ◽  
Inverse Model ◽  
Iterative Learning ◽  
Compensation Method ◽  
Tracking Accuracy ◽  
Linear Motors ◽  
Learning Speed ◽  
Force Ripple

Permanent magnet linear motors (PMLMs) are gaining increasing interest in ultra-precision and long stroke motion stage, such as reticle and wafer stage of scanner for semiconductor lithography. However, the performances of PMLM are greatly affected by inherent force ripple. A number of compensation methods have been studied to solve its influence to the system precision. However, aiming at some application, the system characteristics limit the design of controller. In this paper, a new compensation strategy based on the inverse model iterative learning control and robust disturbance observer is proposed to suppress the influence of force ripple. The proposed compensation method makes fully use of not only achievable high tracking accuracy of the inverse model iterative learning control but also the higher robustness and better iterative learning speed by using robust disturbance observer. Simulation and experiments verify effectiveness and superiority of the proposed method.

Iterative learning control of robot based on artificial bee colony algorithm

2019 ◽  
Vol 233 (9) ◽  
pp. 1221-1238
Author(s):  
Wanqiang Xi ◽  
Yaoyao Wang ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Iterative Learning Control ◽  
Degrees Of Freedom ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Industrial Robots ◽  
Control Effect ◽  
Bee Colony

For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimized through the exchange of information and the survival of fittest between them. And then the optimized results are returned to iterative learning control algorithm. Finally, the digital simulation of a two-degrees-of-freedom manipulator and the experimental verification of a cable-driven robot with its first two joints are carried out. The results show that the iterative learning control based on the artificial bee colony algorithm has faster convergence and better control effect than the iterative learning control with fixed parameters.

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