Fuzzy Iterative Learning Control of Servo System

2011 ◽  
Vol 217-218 ◽  
pp. 917-923
Author(s):  
Hong Li Liu ◽  
Qi Xin Zhu ◽  
Xiang Wen Chen
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Fuzzy Controller ◽  
Design Method ◽  
Servo System ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Control Performance ◽  
System A

According to the repeatability characteristics of servo system, a fuzzy iterative learning controller is proposed which combining advantages of fuzzy control and iterative learning control. Fuzzy control has better robustness, and does not require accurate model of the system, only need the previous experience, the design method is simple. Fuzzy controller is used in the position-loop, but the static error is difficult to eliminate, iterative learning controller use the control error to adjust the previous control input to reduce the error of next time. The combination of these two intelligent control algorithms can improve the control performance of servo system effectively. Simulation results show that the fuzzy control combined with iterative learning control can achieve better control performance in servo system.

scholarly journals Research On Adaptive Optimal Iterative Learning Control Based on Least Squares

2021 ◽  
Author(s):  
Liang-Liang Yang ◽  
Xiang Luo ◽  
Rui Yuan ◽  
Hui Zhang
Keyword(s):  
Least Squares ◽  
Iterative Learning Control ◽  
Servo System ◽  
Learning Control ◽  
Iterative Learning ◽  
Tracking Performance ◽  
System Model ◽  
Time Varying ◽  
Parameter Perturbation ◽  
System A

Abstract Traditional Optimal Iterative Learning Control (TOILC) can effectively improve the tracking performance of the servo system. However, there may be parameter perturbation in the running process of the servo system, and its parameters are constantly changing slowly. As a result, the convergence of TOILC becomes worse, and the tracking performance of the system deteriorates seriously. Therefore, in view of the time-varying characteristics of the system, a least squares optimal iterative learning control (LSAOILC) algorithm is proposed. In the process of iteration, the nominal model of the system is identified according to the input and output signals so as to update the optimal iterative learning controller, which does not need to obtain the exact system model information in advance, making up for the shortage of TOILC. The simulations and experiments prove the effectiveness of the proposed strategy for the servo system.

A Two-Step Optimization-Based Iterative Learning Control for Quadrotor Unmanned Aerial Vehicles

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Revant Adlakha ◽  
Minghui Zheng
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Design Problem ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Aerial Vehicles ◽  
Quadrotor Unmanned Aerial Vehicles ◽  
Optimization Based Design

Abstract This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

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.

scholarly journals Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Yun-Shan Wei ◽  
Qing-Yuan Xu
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Initial State ◽  
Tracking Errors ◽  
Discrete Time Systems ◽  
P Type ◽  
Time Systems

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

scholarly journals Iterative learning control with applications in energy generation, lasers and health care

2016 ◽  
Vol 472 (2193) ◽  
pp. 20150569 ◽  
Author(s):  
E. Rogers ◽  
O. R. Tutty
Keyword(s):  
Health Care ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Free Electron Lasers ◽  
Time Duration ◽  
Physical Systems ◽  
Mode Of Operation ◽  
Duration Task

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

Iterative Learning Control for a Class of Modeling Undesirable Single Degree of Freedom System

2014 ◽  
Vol 538 ◽  
pp. 379-382
Author(s):  
Wei Zhou ◽  
Bao Bin Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Degree Of Freedom ◽  
Control Input ◽  
Convergence Properties ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Simulation Results

A class of modeling undesirable single degree of freedom system is studied by using iterative learning control. The proposed iterative learning algorithm constantly updates the control input according to output error until the desired output occurred. So the system with designed controller can achieve perfect accuracy. We have proved convergence properties in iteration domain and simulation results demonstrate the effectiveness of the presented method.

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