Add-on-type Robust Iterative Learning Controller Design Based on the Information of Feedback Control Systems

Tae-Yong Doh,Jung Rae Ryoo 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.5

Iterative learning control (ILC) combined with a feedback control system improves tracking performance by iteratively tuning the feedforward control signal on the basis of the system information, such as control inputs and tracking errors from previous iterations. Although ILC systems have been added to the existing feedback control systems, the learning controllers have been designed without considering valuable information, such as weighting functions used to design a robust feedback controller. This paper proposes a method for the design of an add-on-type robust iterative learning controller for an uncertain feedback control system using its explicit tracking-performance and plant-uncertainty information. The proposed ILC system is composed of two learning controllers, one of which is directly obtained from the inverse of the nominal feedback control system, and the other is a low-pass filter, known as the Q-filter ensuring robustness for the convergence under uncertainty. To design the learning controllers, first, a robust convergence condition in the L2-norm sense is formulated, which is represented as the Q-filter and other known system information. Subsequently, the sufficient conditions to ensure that the remaining error is less than the initial error are derived. From the results, the criteria for simply designing the learning controllers are presented. Finally, important properties of the proposed ILC system, such as convergence rate and robustness, are demonstrated through simulations.

Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset

Xu Yongjia,Lu Xinzheng,Fei Yifan,Huang Yuli 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5

There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learning method for training neural networks based on small datasets is proposed in this study. A new mapping-based transfer learning network, named as deep adaptation network with three branches for regression (DAN-TR), is proposed. A general iterative network training strategy is developed by coupling DAN-TR and the pseudo-label strategy, and the establishment of corresponding datasets is also discussed. Finally, a complex component is selected as a case study. The results show that the proposed method can improve the model performance by near an order of magnitude on small datasets without the need of external labeled samples, well behaved pre-trained models, additional artificial labeling, and complex physical/mathematical analysis.

PID Type Iterative Learning Control with Optimal Gains

Ali Madady 대한전기학회 2008 International Journal of Control, Automation, and Vol.6 No.2

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PLD coefficients. An optimal design method is proposed to determine the PLD coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

Iteration domain H_∞-optimal iterative learning controller design

Moore, Kevin L.,Ahn, Hyo-Sung,Chen, Yang Quan John Wiley Sons, Ltd. 2008 INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONT Vol.18 No.10

<P>This paper presents an H<SUB>∞</SUB>-based design technique for the synthesis of higher-order iterative learning controllers (ILCs) for plants subject to iteration-domain input/output disturbances and plant model uncertainty. Formulating the higher-order ILC problem into a high-dimensional multivariable discrete-time system framework, it is shown how the addition of input/output disturbances and plant model uncertainty to the ILC problem can be cast as an H<SUB>∞</SUB>-norm minimization problem. The distinctive feature of this formulation is to consider the uncertainty as arising in the iteration domain rather than the time domain. An algebraic approach to solving the problem in this framework is presented, resulting in a sub-optimal controller that can achieve both stability and robust performance. The key observation is that H<SUB>∞</SUB> synthesis can be used for higher-order ILC design to achieve a reliable performance in the presence of iteration-varying external disturbances and model uncertainty. Copyright © 2007 John Wiley & Sons, Ltd.</P>

Diesel Generator Speed Control Based on Variable Forgetting Factor Iterative Learning Method

A. Manlei Huang,B. Xinglei He 전력전자학회 2023 ICPE(ISPE)논문집 Vol.2023 No.-

This paper applies the iterative learning control (ILC) method to the speed control system of the diesel generator set in the marine power system. Compared with traditional PID, ILC has good control performance in response speed and disturbance resistance. The variable forgetting factor is added to ILC to optimize the control signal oscillation with the number of iterations. Iterative learning control with forgetting factor (ILCFF) shows good control effect by simulating the increase and decrease of the load on the engine.

An Iterative Learning Control Approach for Linear Time-invariant Systems with Randomly Varying Trial Lengths

Xuefang Li,Jian-Xin Xu,Deqing Huang 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10

This paper addresses an iterative learning control (ILC) design problem for discrete-time linear systems where the trial lengths could be randomly varying in the iteration domain. An ILC scheme with an iteration-average operator is introduced for tracking tasks with non-uniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. The learning convergence condition of ILC in mathematical expectation is derived through rigorous analysis. As a result, the proposed ILC scheme is applicable to more practical systems. In the end, an illustrative example is presented to demonstrate the performance and the effectiveness of the averaging ILC scheme.

2nd-order PD-type Learning Control Algorithm

Yong-Tae Kim(김용태),Zeungnam Bien(변증남) 한국지능시스템학회 2004 한국지능시스템학회논문지 Vol.14 No.2

In this paper are proposed 2nd-order PD-type iterative learning control algorithms for linear continuous-time system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time-domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithms.

Adaptive Iterative Learning Trajectory Tracking Control for Spraying Manipulator With Arbitrary Initial States and Iteration-varying Reference Trajectory

Ting Zhang,Xiaohong Jiao,Zhong Wang 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.6

This paper presents a novel adaptive iterative learning control (AILC) scheme to improve the trackingperformance of the manipulator for spraying hull to achieve high-quality spraying effectively. First, a novel way of modifying the reference trajectory is proposed to deal with arbitrary initial states and iteration-varying referencetrajectory when the manipulator reciprocates spraying. The modified way comprises constructing an error variable containing an initial correction term and determining the shortest limited time of completely tracking the desired trajectory by optimization principle. Based on this, an AILC scheme uses adaptive and backstepping techniques to handle the system’s uncertain physical parameters and external disturbance. Theoretically, this control scheme can guarantee the tip-position of the spraying manipulator to perfectly track the desired reference trajectory within an appropriate, limited time under arbitrary initial states and iteration-varying reference trajectory. Simulations and experiments verify the proposed method’s effectiveness and advantage by comparison with other control algorithms.

An LMI Approach to Robust Iterative Learning Control for Linear Discrete-time Systems

Mojtaba Ayatinia,Mehdi Forouzanfar,Amin Ramezani 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.7

This paper presents a new robust convergence condition of iterative learning control (ILC) for linear multivariable discrete-time systems in the presence of iteration-varying uncertainty. This method is based on linear matrix inequality (LMI) and provides a fixed learning gain over time and iteration. Since the convergence of the ILC algorithm may change due to uncertainty in the parameters of a system, and the ILC algorithm is incapable of dealing with iteration-related challenges, it is a major challenge to reject the effect of iteration varying uncertainty. In this paper, first, a convergence condition of the ILC algorithm is designed based on closed-loop system stability in the iteration domain, and second, a new robust convergence condition is achieved by the LMI approach. Finally, the effectiveness of the proposed robust convergence scheme is evaluated through two numerical examples.

Mechanical Ventilator Pressure and Volume Control Using Classifier Machine Learning Algorithm for Medical Care

Anitha T.,Gopu G.,Arun Mozhi Devan P. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.4

The mechanical ventilation technique is crucial for saving the lives of critically ill patients in the Intensive Care Unit. However, there can be a mismatch between the patient’s needs and the ventilator settings, which can cause patient-ventilator asynchrony. Our research aims to tackle this issue by implementing a novel current cyclic feedback type iterative learning PID controller (ILCPID) to achieve the desired pressure and volume. The research also provides a concise and comprehensive study to identify the most eff ective machine learning methodology for developing adequate ventilation models. In addition, the ILCPID controller’s accuracy and eff ectiveness are further validated using machine learning-based classifi ers that can predict and identify the best model for the diff erent ventilator modes, reducing the risk of mechanically ventilated patients. Among the diff erent classifi ers, the proposed narrow neural network achieved an accuracy of 92.4% and 89.29% for pressure and volume, respectively. Other techniques, such as wide neural networks, coarse trees, K-nearest neighbours, and decision trees, were also compared for accuracy, training duration, specifi city, and sensitivity.