scholarly journals Iterative Learning Control for Switched Systems with Sensor Saturation Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wei Cao ◽  
Jinjie Qiao ◽  
Ming Sun
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Control Variable ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Time Interval ◽  
Actual Measurement ◽  
Sensor Saturation ◽  
Saturation Constraints

To solve trajectory tracking problem of switched system with sensor saturation, an iterative learning control algorithm is proposed. The method uses actual measurement error to modify the control variable of system on the premise that switched rule does not change along iteration axis, but it randomly changes along time axis. Moreover, by dealing with the saturation via diagonal matrix method, the convergence of the algorithm is strictly proved in the sense of λ-norm, and the convergence condition is derived. The algorithm can achieve complete tracking of desired trajectory in the finite time interval under the random switched rule, as iterations increase. The simulation example verifies the validity of the proposed algorithm.

scholarly journals A PD-Type Iterative Learning Control for a Class of Switched Discrete-Time Systems with Model Uncertainties and External Noises

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xuan Yang
Keyword(s):  
Discrete Time ◽  
Switched Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Monotone Convergence ◽  
Model Uncertainties ◽  
Time Systems

A PD-type iterative learning control algorithm is applied to a class of linear discrete-time switched systems for tracking desired trajectories. The application is based on assumption that the switched systems repetitively operate over a finite time interval and the switching rules are arbitrarily prespecified. By taking advantage of the super-vector approach, a sufficient condition of the monotone convergence of the algorithm is deduced when both the model uncertainties and the external noises are absent. Then the robust monotone convergence is analyzed when the model uncertainties are present and the robustness against the bounded external noises is discussed. The analysis manifests that the proposed PD-type iterative learning control algorithm is feasible and effective when it is imposed on the linear switched systems specified by the arbitrarily preset switching rules. The attached simulations support the feasibility and the effectiveness.

scholarly journals An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Saleem Riaz ◽  
Hui Lin ◽  
Muhammad Waqas ◽  
Farkhanda Afzal ◽  
Kai Wang ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Position Control ◽  
Tracking Error ◽  
Learning Control ◽  
Convergence Condition ◽  
Design Criterion ◽  
Iterative Learning ◽  
Current Error ◽  
P Type

Traditional and typical iterative learning control algorithm shows that the convergence rate of error is very low for a class of regular linear systems. A fast iterative learning control algorithm is designed to deal with this problem in this paper. The algorithm is based on the traditional P-type iterative learning control law, which increases the composition of adjacent two overlapping quantities, the tracking error of previous cycle difference signals, and the current error difference. Using convolution to promote Young inequalities proved strictly that, in terms of Lebesgue-p norm, when the number of iterations tends to infinity, the tracking error converges to zero in the system and presents the convergence condition of the algorithm. Compared with the traditional P-type iterative learning control algorithm, the proposed algorithm improves convergence speed and evades the defect using the norm metric’s tracking error. Finally, the validation of the effectiveness of the proposed algorithm is further proved by simulation results.

scholarly journals Robust Iterative Learning Control for a Class of Linear Systems with Data Dropouts

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Rui ◽  
Ma Xinghe ◽  
Bu Xuhui
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Contraction Mapping ◽  
Initial Conditions ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Control Input ◽  
Initial Condition ◽  
Data Dropouts

A robust iterative learning control algorithm is proposed for a class of intermittent systems with disturbances and uncertain initial conditions. Based on the contraction mapping approach, the convergence condition for the proposed algorithm is first given, and then the bounds on control input and output trajectories can be obtained. It is shown that these bounds depend on bounds on the initial condition errors and disturbances, and the bounds are zero in the absence of these disturbances. A numerical example is also given to verify the theoretical result.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

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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