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Design and Experimental Development of Wireless iterative learning Fault Estimation algorithm With Quantization and Packet Losses

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IEEE ACCESS 2021年 9卷 150120-150127页

作者： Herve, Samba Aime Aurelien, Yeremou Tamtsia Leandre, Nneme Nneme Som, Idellette Judith Hermine Alphonse, Houwe Univ Douala Natl Higher Polytech Sch Douala Cameroon Univ Douala Adv Teachers Training Coll Tech Educ Douala Cameroon Univ Maroua Fac Sci Maroua Cameroon

In this paper, a wireless iterative learning fault estimation algorithm (WILFEA) is proposed and validated experimentally with the aim to achieve perfect tracking of a prescribed reference trajectory for systems with packet losses and quantizer measurements that operate repetitively. First, state variables, Markov chain process of random packet losses, and a logarithmic quantizer are considered to establish an extended-state-space system model. Next, based on this model, sufficient conditions for linear repetitive processes are developed with the Lyapunov-Krasovskii technique and H-infinity approach is applied to calculate the observer gain and the learning gain. Then, WILFEA based fault estimation is constructed. Compared with the existing methods, the proposed WILFEA improves the fault estimation performance in the current iteration by consider both state error and fault estimation error. Finally, the simulation and experimental results are used for DC-Servomotor system to illustrate the effectiveness of the proposed approach using Matlab/simulink software, LabVIEW Software, ZigBee Xbee and Arduino board.

关键词： Estimation Packet loss Quantization (signal) Heuristic algorithms iterative algorithms Delays Wireless communication iterative learning algorithm quantiser measurements random packet losses fault estimation

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Enhancing Accuracy and Numerical Stability for Repetitive Time-Varying System Identification: An iterative learning Approach

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IEEE ACCESS 2020年 8卷 25679-25690页

作者： Song, Fazhi Liu, Yang Wang, Xianli Jin, Wen Li, Li Harbin Inst Technol Ctr Ultraprecis Optoelect Instrument Engn Harbin 150080 Peoples R China Minist Ind & Informat Technol Key Lab UltraPrecis Intelligent Instrumentat Harbin 150080 Peoples R China Luoyang Normal Univ Coll Phys & Elect Informat Luoyang 471934 Peoples R China Harbin Med Univ Sch Pharmaceut Sci Harbin 150081 Peoples R China

Time-varying system identification is an appealing but challenging research area. Existing identification algorithms are usually subject to either low estimation accuracy or bad numerical stability. These deficiencies motivate the development of an iterative learning identification algorithm in this paper. Three distinguished features of the proposed method result in the achievement of high estimation accuracy and high numerical stability: i) recursion along the iteration axis, ii) bias compensation, and iii) singular value decomposition (SVD). Firstly, an extra iteration axis associated with the original time axis is introduced in the parameter estimation process. A norm-optimal identification approach with the balance between convergence speed and noise robustness is then proposed along the iteration axis, followed by further analysis on the accuracy and the numerical stability. Secondly, in order to eliminate the estimation bias in the presence of noise and thus to improve the accuracy, a bias compensation algorithm along the iteration axis is proposed. Thirdly, a SVD-based update algorithm for the covariance matrix is developed to avoid the possible numerical instability during iterations. Numerical examples are finally provided to validate the algorithm and confirm its effectiveness.

关键词： iterative learning algorithm time-varying system parameter estimation output-error system bias compensation singular value decomposition

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Fourier series based learning control and application to positioning table

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ROBOTICS AND AUTONOMOUS SYSTEMS 2000年第2-3期32卷 89-100页

作者： Cai, L Huang, W Hong Kong Univ Sci & Technol Dept Mech Engn Kowloon Hong Kong Peoples R China

A new Fourier series based learning control scheme is presented in this paper. The proposed controller consists of two parts: a time domain feedback controller that is designed to stabilize the system and improve the robustness to random disturbance, and a Fourier series based learning controller that is used to generate the best feedforward in the face of deterministic modeling uncertainties. The learning controller is essentially a feedback controller in frequency domain. A new iterative algorithm based on the Fourier series approximation generates the optimal feedforward to force the state trajectory converge to a stable sliding surface. Only the historical input and output information of the closed-loop system is used. There is no requirement for knowledge about the system structure and parameters. The stability analysis of the closed-loop system with the learning controller is also provided. The effectiveness of the proposed controller is experimentally verified on a positioning table. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： Fourier series learning control iterative learning algorithm time domain feedback control closed-loop system positioning table

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Singular value decomposition based learning identification for linear time-varying systems: From recursion to iteration

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第12期33卷 6986-7003页

作者： Song, Fazhi Li, Li Liu, Yang Dong, Yue Harbin Inst Technol Ctr Ultra Precis Optoelect Instrument Engn Harbin Peoples R China Minist Ind & Informat Technol Harbin Inst Technol Key Lab Ultra Precis Intelligent Instrumentat Harbin Peoples R China Harbin Inst Technol Ctr Ultraprecis Optoelect Instrument Engn 92Xidazhi St Harbin Peoples R China

System identification is a critical task in various engineering applications such as motion control, signal processing and robotics. In this article, the identification of linear time-varying (LTV) systems that perform tasks repetitively over a finite-time interval is investigated. Traditional LTV system identification typically adopts recursive algorithms in the time domain, which are incapable of tracking drastic-varying parameters and are subject to estimation lag and numerical instability. To address these issues, this article proposes the utilization of an iteration axis in addition to the time axis for estimating repetitive time-varying parameters. Specifically, the proposed approach involves an estimation algorithm for the time-varying parameters based on a recursive least squares (RLS) method along the iteration axis, as well as an update algorithm for the covariance matrix based on singular value decomposition (SVD) to enhance numerical stability. Additionally, a bias compensation method based on noise variance estimation is introduced for the sake of eliminating estimation error induced by measurement noise. Numerical comparisons with existing methods are conducted to demonstrate the effectiveness and superiority of the proposed method.

关键词： bias compensation iterative learning algorithm parameter estimation singular value decomposition time-varying systems

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learning impedance control for robotic manipulators

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IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1998年第3期14卷 452-465页

作者： Cheah, CC Wang, DW Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

learning control is a concept for controlling dynamic systems in an iterative manner. It arises from the recognition that robotic manipulators are usually used to perform repetitive tasks. Most researches on the iterative learning control of robots have been focused on the problem of free motion control and hybrid position/force control where the learning controllers are designed to track the desired motion and force trajectories. The iterative learning impedance control of robotic manipulators, however, has been studied recently. In this paper, an iterative learning impedance control problem for robotic manipulators is formulated and solved. A target impedance is specified and a learning controller is designed such that the system follows the desired response specified by the target model as the actions are repeated, A design method for analyzing the convergence of the learning impedance system is developed. A sufficient condition for guaranteeing the convergence of the system is also derived, The proposed learning impedance control scheme is implemented on an industrial selective compliance assembly robot arm (SCARA) robot, SEIKO TT3000. Experimental results verify the theory and confirm the effectiveness of the learning impedance controller.

关键词： convergence analysis impedance control iterative learning algorithm robot force control

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Novel iterative Machine learning for Accurate Photon-Counting Poisson Channel Estimation

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IEEE COMMUNICATIONS LETTERS 2022年第9期26卷 2081-2085页

作者： Arya, Sudhanshu Chung, Yeon Ho Pukyong Natl Univ Dept Artificial Intelligence Convergence Busan 48513 South Korea Pukyong Natl Univ Dept Informat & Commun Engn Busan 48513 South Korea

In this work, we present a new machine learning-based framework for the accurate estimation of the shot-noise limited photon-counting Poisson channel by developing a state-of-art iterative unsupervised learning algorithm for intelligent optical communications. By accurate estimation, we mean that it achieves very low estimation error even when the effect of the received data is unpredictable. We consider a realistic situation where modulated symbols are assumed to be hidden or unobserved latent variables, thereby making conventional estimation algorithms based on maximum likelihood approach unsuitable or inefficient. In particular, we consider a probabilistic model and assume that the received data is not labeled. With this unpredictable data considered, a novel iterative machine learning framework is developed based on an expectation and maximization algorithm. The proposed algorithm avoids the need to choose an appropriate step size as required in gradient method based algorithms. It is shown that it significantly outperforms the least square and the Viterbi detection technique.

关键词： Estimation Photonics Machine learning algorithms Channel estimation Wireless communication iterative algorithms High-speed optical techniques iterative learning algorithm Poisson channel optical wireless communications unsupervised learning

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Design of learning input shaping technique for residual vibration suppression in an industrial robot

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2006年第1期11卷 55-65页

作者： Park, J Chang, PH Park, HS Lee, E Daewoo Shipbldg & Marine Engn Co Ltd Koje 656714 South Korea Korea Adv Inst Sci & Technol Taejon 305701 South Korea Rehabil Inst Chicago Chicago IL 60611 USA

In this paper, a practical method is proposed to suppress residual vibrations of industrial robots without a real-time estimation of vibration frequencies. Through theoretical analysis and experiments, we designed an input shaping technique (IST) for the first three axes of a six-degrees-of-freedom industrial robot. iterative learning IST (LIST) is applied to the first axis to suppress its time-varying nonlinear residual vibration, while conventional IST is applied to the second and third axes. Experimental results show that LIST can suppress residual vibrations to a level similar to that of a time-varying IST which requires complicated real-time estimation of a dynamic model. The LIST is an attractive method for suppression of nonlinear and time-varying residual vibrations in industrial robots which perform repetitive tasks because most industrial robots have limited computing power and memory space in their controllers.

关键词： flexible structure industrial robot input shaping iterative learning algorithm residual vibration

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Vehicle Automatic iterative learning Control based on Drivers' Starting Behavior

Vehicle Automatic Iterative Learning Control based on Driver...

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Chinese Automation Congress (CAC)

作者： Gao, Zhenhai Sun, Tianjun Zhou, Shuhui Wang, Xiaohan Mei, Xingtai Jilin Univ Coll Automot Engn Changchun Peoples R China Chinese Acad Sci Shenyang Inst Automat Project Planning Shenyang Peoples R China Guangzhou Automobile Grp Co Ltd Automot Engn Res Inst Guangzhou Peoples R China

ISBN: (纸本)9781728140940

Early studies on intelligent control methods for automatic starting control for vehicles mainly focus on traditional parametric adjustment. However, attempts toward the combination of learning algorithms and models are rare. When drivers' starting behavior is not considered and tedious parameters are merely used for drive control, the effects result in discomfort for drivers. Therefore, to imitate drivers' starting behavior when dealing with automatic drive control in vehicles, we must first develop an acceleration fitting based on DFT by analyzing the starting characteristics. Then, we design an iterative learning algorithm to achieve automatic starting control of vehicles. Finally, a simulation test is conducted based on CARSIM to verify the validity and feasibility of the proposed method.

关键词： vehicle starting control driving behavior iterative learning algorithm Discrete Fourier Transform

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Hardware-in-the-Loop Simulation for Active Force Control with iterative learning Applied to an Active Vehicle Suspension System

Hardware-in-the-Loop Simulation for Active Force Control wit...

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4th International Conference on Mechanical and Manufacturing Engineering (ICME 2013)

作者： Rosli, Rosmazi Mailah, Musa Priyandoko, Gigih Univ Malaysia Pahang Fac Mech Engn Pekan Pahang Malaysia Univ Teknol Malaysia Fac Mech Engn Skudai Malaysia

ISBN: (纸本)9783037859339

The paper focuses on the practical implementation of a novel control method to an automotive suspension system using active force control (AFC) with iterative learning algorithm (ILA) and proportional-integral-derivative (PID) control strategy. The overall control system to be known as AFC-IL scheme essentially comprises three feedback control loops to cater for a number of specific tasks, namely, the innermost loop for the force tracking of the pneumatic actuator using PI controller, intermediate loops applying AFC with ILA strategy for the compensation of the disturbances and the outermost loop using MD controller for the computation of the desired force. A number of experiments were carried out on a physical test rig with hardware-in-the-loop simulation (HILS) feature that fully incorporates the theoretical elements. The performance of the proposed control method was evaluated and benchmarked to examine the effectiveness of the system in suppressing the vibration effect of the suspension system. It was found that the experimental results demonstrate the superiority of the active suspension system with proposed AFC-IL scheme compared to the PID and passive counterparts.

关键词： Active force control iterative learning algorithm active suspension hardware-in-the-loop simulation

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A Controller Design based on iterative learning method and Model Predictive Control for a nonlinear process system 6

A Controller Design based on Iterative Learning method and M...

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6th International Conference on Control, Instrumentation and Automation (ICCIA)

作者： Zamani, Mohammad Reza Rahmani, Zahra Rezaee, Behrooz Babol Noshirvani Univ Technol Elect & Comp Engn Dept Babol Iran

ISBN: (纸本)9781728158150

This paper proposes a controller using model predictive control and iterative learning control algorithm for a class of nonlinear process. A predictive control model computes system outputs, and accurate prediction is the desired purpose. Model predictive controller relies on dynamic models of a system, linear empirical models are mostly derived from system identification. In order to control a system accurately, an accurate model is needed. These control purposes can be achievable by using iterative learning algorithm. This type of modeling is applied for the first time in this paper for a nonlinear process. iterative learning algorithm improves the performance of the processes that perform the same motion or operation repeatedly. This proposed method is capable of improving the accuracy of a model and the robustness of this method in presence of repetitive disturbance. The rejection of repetitive disturbance and deterministic modeling error which caused from repetitive disturbances by iterative learning algorithm has been proved. The proposed method is used for controlling liquid level in two-tank The simulation results show the effectiveness of the proposed method.

关键词： Control theory iterative learning algorithm Numerical modeling Predictive control Simulation systems identification

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