scholarly journals Iterative Learning Control for Fractional order Nonlinear System with Initial Shift

2021 ◽  
Author(s):  
Zhou Fengyu ◽  
Wang Yugang
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Closed Loop ◽  
Learning Control ◽  
Iterative Learning ◽  
Fractional Order Systems ◽  
Tracking Errors ◽  
Fractional System ◽  
The Time Domain ◽  
Fractional Order Nonlinear System

Abstract In this study, a closed-loop Dα -type iterative learning control(ILC) with a proportional D-type iterative learning updating law for the initial shift is applied to nonlinear conformable fractional system. First, the system with the initial shift is introduced. Then, fractional-order ILC (FOILC) frameworks that experience the initial shift problem for the path-tracking of nonlinear conformable fractional order systems are addressed. Moreover, the sufficient condition for the convergence of tracking errors is obtained in the time domain by introducing λ -norm and Hölder’s inequality. Lastly, numerical examples are provided to illustrate the effectiveness of the proposed methods.

Robust adaptive sliding mode control combination with iterative learning technique to output tracking of fractional-order systems

2017 ◽  
Vol 40 (6) ◽  
pp. 1808-1818 ◽  
Author(s):  
Ehsan Ghotb Razmjou ◽  
Seyed Kamal Hosseini Sani ◽  
Jalil Sadati
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Sliding Mode ◽  
External Disturbance ◽  
Learning Control ◽  
Iterative Learning ◽  
Adaptive Sliding Mode Control ◽  
Order System ◽  
Control Input ◽  
Fractional Order Systems

This paper develops a novel controller called adaptive iterative learning sliding mode (AILSM) to control linear and nonlinear fractional-order systems. This controller applies a hybrid structure of adaptive and iterative learning control in to sliding mode method. It can switch between both adaptive and iterative learning control in order to use the advantages of both controllers simultaneously and therefore achieve better control performance. This controller is designed in a way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order system. The proposed controller, unlike the conventional iterative learning control, does not need to apply direct control input to output of the system. It is shown that the controller performs well in partial and complete observable conditions. Illustrative examples verify the performance of the proposed control in presence of unknown disturbances and model uncertainties.

scholarly journals Fractional-Order Iterative Learning Control with Initial State Learning for a Class of Multiagent Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xungen Li ◽  
Shuaishuai Lv ◽  
Mian Pan ◽  
Qi Ma ◽  
Wenyu Cai
Keyword(s):  
Multiagent Systems ◽  
Fractional Order ◽  
Iterative Learning Control ◽  
Closed Loop ◽  
Learning Control ◽  
Iterative Learning ◽  
Initial State ◽  
Convergence Conditions ◽  
Fractional Order Calculus ◽  
State Learning

To solve the consensus problem of fractional-order multiagent systems with nonzero initial states, both open- and closed-loop PDα-type fractional-order iterative learning control are presented. Considering the nonzero states, an initial state learning mechanism is designed. The finite time convergences of the proposed methods are discussed in detail and strictly proved by using Lebesgue-p norm theory and fractional-order calculus. The convergence conditions of the proposed algorithms are presented. Finally, some simulations are applied to verify the effectiveness of the proposed methods.

Further results on advanced robust iterative learning control and modeling of robotic systems

2020 ◽  
pp. 095440622096599
Author(s):  
Mihailo P Lazarević ◽  
Petar D Mandić ◽  
Srđan Ostojić
Keyword(s):  
Iterative Learning Control ◽  
Feedback Linearization ◽  
Angular Displacement ◽  
Viscous Friction ◽  
Learning Control ◽  
Iterative Learning ◽  
Revolute Joints ◽  
General Order ◽  
Robotic Joints ◽  
The Time Domain

Recently, calculus of general order [Formula: see text] has attracted attention in scientific literature, where fractional operators are often used for control issues and the modeling of the dynamics of complex systems. In this work, some attention will be devoted to the problem of viscous friction in robotic joints. The calculus of general order and the calculus of variations are utilized for the modeling of viscous friction which is extended to the fractional derivative of the angular displacement. In addition, to solve the output tracking problem of a robotic manipulator with three DOFs with revolute joints in the presence of model uncertainties, robust advanced iterative learning control (AILC) is introduced. First, a feedback linearization procedure of a nonlinear robotic system is applied. Then, the proposed intelligent feedforward-feedback AILC algorithm is introduced. The convergence of the proposed AILC scheme is established in the time domain in detail. Finally, simulations on the given robotic arm system confirm the effectiveness of the robust AILC method.

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