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IEEE/CAA Journal of Automatica Sinica 2021年第9期8卷 1576-1587页

作者： Tongfu He Zhong Wu School of Instrumentation and Optoelectronics Engineering Beihang UniversityBeijing 100191China

To realize high-precision attitude stabilization of a flexible spacecraft in the presence of complex disturbances and measurement noises,an iterative learning disturbance observer(ILDO)is presented in this ***,a dynamic model of disturbance is built by augmenting the integral of the lumped disturbance as a *** on it,ILDO is designed by introducing iterative learning ***,comparative analyses of ILDO and traditional disturbance observers are carried out in frequency *** demonstrates that ILDO combines the advantages of high accuracy in disturbance estimation and favorable robustness to measurement *** that,an ILDO based composite controller is designed to stabilize the spacecraft ***,the effectiveness of the proposed control scheme is verified by simulations.

关键词： Disturbance observer iterative learning measure-ment noise spacecraft attitude control

iterative learning based fault diagnosis for discrete linear uncertain systems

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Journal of Systems Engineering and Electronics 2014年第3期25卷 496-501页

作者： Wei Cao Ming Sun College of Computer and Control Engineering Qiqihar University

In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.

关键词： discrete linear uncertain system threshold limited technology iterative learning fault estimation.

iterative learning based fault detection and estimation in nonlinear systems

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Journal of Systems Engineering and Electronics 2012年第3期23卷 419-424页

作者： Wei Cao Wang Cong Ming Sun College of Automation Harbin Engineering University Harbin 150001 E R. China College of Computer and Control Engineering Qiqihar University Qiqihar 161006 E R. China

Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the difference- value signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking con- vergent speed than the proportional-type （P-type） algorithm, but also calculates more simply than the proportional-derivative-type （PD-type） algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.

关键词： iterative learning nonlinear system fault detection fault estimation.

iterative learning-based many-objective history matching using deep neural network with stacked autoencoder

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Petroleum Science 2021年第5期18卷 1465-1482页

作者： Jaejun Kim Changhyup Park Seongin Ahn Byeongcheol Kang Hyungsik Jung Ilsik Jang Department of Energy Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Energy and Resources Engineering Kangwon National UniversityChuncheonKangwon24341Republic of Korea Geo-ICT Convergence Research Team Korea Institute of Geoscience and Mineral ResourcesDaejeon34132Republic of Korea Department of Energy and Resources Engineering Chosun UniversityGwangju61452Republic of Korea

This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history *** proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering *** functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs *** iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration *** proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population *** learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata *** confirms the proposed workflow constructs more plausible *** workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.

关键词： Deep neural network Stacked autoencoder History matching iterative learning Clustering Many-objective

Trajectory tracking control of a bionic robotic fish based on iterative learning

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Science China(Information Sciences) 2020年第7期63卷 79-87页

作者： Ming WANG Yanlu ZHANG Huifang DONG Junzhi YU School of Information and Electrical Engineering Shandong Jianzhu University State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESAT College of Engineering Peking University

A bionic robotic fish has great potential application prospect. High maneuverability swimming control of a bionic robotic fish has been one of the research hotspots in the robotic fish field. In this paper,an iterative learning method has been proposed to solve the trajectory tracking control problem of robotic fish swimming. First, a dynamic model of the multi-joint bionic robotic fish is established. By considering a three-joint robotic fish as an example, the unified expression of the dynamic equation of the three-joint bionic robotic fish is obtained by Lagrange method. Second, the iterative learning controller for controlling the bionic robotic fish is designed. Then the convergence of the iterative learning controller is ***, the trajectory tracking control simulation experiment based on iterative learning is conducted. The simulation results show that the trajectory tracking control method based on iterative learning for a bionic robotic fish is effective.

关键词： bionic robot fish iterative learning trajectory tracking motion control

iterative learning Model Predictive Control for a Class of Continuous/Batch Processes

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Chinese Journal of Chemical Engineering 2009年第6期17卷 976-982页

作者：周猛飞王树青金晓明张泉灵 State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

关键词： continuous/batch process model predictive control event monitoring iterative learning soft constraint

On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) iterative learning iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

Robust iterative learning Control of Single-phase Grid-connected Inverter

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International Journal of Automation and computing 2014年第4期11卷 404-411页

作者： Zhong-Qiang Wu Chun-Hua Xu Yang Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

For the single phase inductance-capacitance-inductance(LCL) grid-connected inverter in micro-grid, a kind of robust iterative learning controller is designed. Based on the output power droop characteristics of inverter, the current sharing among the inverters is achieved. iterative learning strategy is suitable for repeated tracking control and inhibiting periodic disturbance, and is designed using robust performance index, so that it has the ability to overcome the uncertainty of system parameters. Compared with the repetitive control, the robust iterative learning control can get high precision output waveform, and enhance the tracking ability for waveform, and the distortion problem of the output signal can be solved effectively.

关键词： Inductance-capacitance-inductance (LCL) filter iterative learning robust control power droop-characteristics repetitive control

Arranging transient process used in iterative learning control system

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Journal of Pharmaceutical Analysis 2009年第1期21卷 42-45页

作者： Li-chuan Hui, Hui Lin School of Automation, Northwestern Polytechnical University, Xi’an 710129, China. School of Automation Northwestern Polytechnical University Xi'an 710129 China

Considering the same initial state error in each repetitive operation in the iterative learning system, a method of arranging the transient process is given. During the current iteration, the system will track the transient function firstly, and then the expected trajectory. After several iterations, the learning system output will trend to the arranged curve, which has avoided the effect of the initial error on the controller. Also the transient time can be changed as you need, which makes the designing simple and the operation easy. Then the detailed designing steps are given via the robot system. At last the simulation of the robot system is given, which shows the validity of the method.

关键词： iterative learning initial state transient process robot system

Fault Estimation for Nonlinear Repetitive Systems: An iterative learning Approach

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Fault Estimation for Nonlinear Repetitive Systems: An Iterat...