A new iterative learning control method for PWM inverter current regulation

2004 ◽  
Author(s):  
L. Ben-brahim ◽  
M. Benammar ◽  
M.A. Athamadi
Keyword(s):  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Current Regulation ◽  
Pwm Inverter

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.

scholarly journals Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Jian Dong ◽  
Bin He
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Control Method ◽  
External Disturbance ◽  
Learning Control ◽  
Iterative Learning ◽  
System Stability ◽  
Learning Gain ◽  
Control Precision ◽  
Quadrotor Aircraft

Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Learning Gain ◽  
Adaptive Iterative Learning Control ◽  
Control Gain ◽  
Unknown Control

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

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 Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram

2020 ◽  
Vol 53 (1) ◽  
pp. 67-87
Author(s):  
Xiaohui Lin ◽  
Jianmin Xu
Keyword(s):  
Feedback Control ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Control Area ◽  
Iterative Learning ◽  
Cumulative Number ◽  
Fundamental Diagram ◽  
Layer Boundary ◽  
Oversaturated Intersections

The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feedforward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.

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