Robust iterative learning control for linear continuous systems with vector relative degree under varying input trail lengths and random initial state shifts

2020 ◽  
Vol 31 (2) ◽  
pp. 609-622
Author(s):  
Yun‐Shan Wei ◽  
Xiao‐Dong Li
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Relative Degree ◽  
Continuous Systems ◽  
Initial State

scholarly journals Iterative Learning Control Design and Application for Linear Continuous Systems with Variable Initial States Based on 2-D System Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Wei Guan ◽  
Qiao Zhu ◽  
Xu-Dong Wang ◽  
Xu-Hui Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Linear Matrix ◽  
Design Criteria ◽  
Continuous Systems ◽  
Matrix Inequalities ◽  
Roesser Model ◽  
Initial State ◽  
Initial States

This paper is concerned with the variable initial states problem in iterative learning control (ILC) for linear continuous systems. Firstly, the properties of the trajectory of 2-D continuous-discrete Roesser model are analyzed by using Lyapunov's method. Then, for any variable initial states which absolutely converge to the desired initial state, some ILC design criteria in the form of linear matrix inequalities (LMI) are given to ensure the convergence of the PD-type ILC rules. The convergence for variable initial states implies that the ILC rules can be used to achieve the perfect tacking for variable initial states, even if the system dynamic is unknown. Finally, the micropropulsion system is considered to illustrate efficiency of the proposed ILC design criteria.

PD-type iterative learning control for linear continuous systems with arbitrary relative degree

2018 ◽  
Vol 41 (9) ◽  
pp. 2555-2562 ◽  
Author(s):  
Qin Fu ◽  
Lili Du ◽  
Guangzhao Xu ◽  
Jianrong Wu ◽  
Pengfei Yu
Keyword(s):  
Iterative Learning Control ◽  
Contraction Mapping ◽  
Learning Control ◽  
Mapping Method ◽  
Iterative Learning ◽  
Relative Degree ◽  
Time Interval ◽  
Continuous Systems ◽  
Iteration Index ◽  
Learning Schemes

This article investigates the iterative learning control problem for linear continuous systems with fixed initial shifts. The systems have arbitrary relative degree and PD-type learning schemes are proposed. Under the effect of the PD-type learning schemes, the output-limiting trajectory is constructed. Based on the contraction mapping method, we show that the schemes can guarantee that the output of the iterative system converges uniformly to the output-limiting trajectory on the finite-time interval as the iteration index tends to infinity. A simulation example is used to illustrate the effectiveness of the proposed method.

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 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.

Sign in / Sign up

Export Citation Format

Share Document