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A novel approach to fault diagnosis for time-delay systems

COMPUTERS & ELECTRICAL ENGINEERING 2014年第7期40卷 2273-2284页

作者： Yan, Bingyong Wang, Huifeng Wang, Huazhong E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

In this paper, a novel fault detection and identification (FDI) scheme for time-delay systems is presented. Different from the existing FDI design methods, the proposed approach utilizes fault tracking approximator (FTA) and iterative learning algorithm to obtain estimates of the fault functions. Performance of the FTA is rigorously analyzed by investigating its stability and fault tracking sensitivity properties in the presence of slowly developing or abrupt faults for state delayed dynamic systems. A novel feature of the FTA is that it can simultaneously detect and identify the shape and magnitude of the faults. Additionally, an extension to a class of nonlinear time-delay systems is made by using nonlinear control theories. Finally, the applicability and effectiveness of the proposed FDI scheme is illustrated by a practical industrial process. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Fault detection and identification iterative learning algorithm Fault tracking approximator Time-delay systems

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A novel model predictive control for a piecewise affine class of hybrid system with repetitive disturbance

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ISA TRANSACTIONS 2021年 108卷 18-34页

作者： Zamani, M. R. Rahmani, Z. Rezaie, B. Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol *** Iran

The present investigation addresses an innovative method based on explicit form of the model predictive control (EMPC) for a constrained Piecewise affine (PWA) class of hybrid systems, considering repetitive disturbance. This model of hybrid systems is investigated due to the fact that PWA modeling structure can approximate nonlinear systems via various operating points, and also because the simulation of PWA models are easy. With EMPC, the problem of optimization is solved in an offline way only once. Unlike conventional EMPC, the process information of the past and the data which are predicted are applied in the proposed strategy. This is the first time that in this study, the investigators adopt an approach in which these predicted data are weighted by another optimization problem (OP) and this weighted predicted sequence along with the past information of the process as an updating control input formula. In fact, two separate OPs are solved simultaneously at each step of proposed EMPC. The first one is linked with calculating the control input from the constrained cost function of EMPC algorithm and the second one concerns finding the optimal weighting factors in order to minimize the error signal, i.e. the difference between the reference path and the output signal at each optimization step of EMPC strategy. The precision of the proposed method is extremely dependent on the accuracy of the process model, so iterative learning control (ILC) algorithm is applied to protecting the process model against the periodic disturbances. These mathematical analyses are proven and validated by simulation results. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Explicit model predictive control iterative learning algorithm Optimal weighting factors Past information Repetitive disturbances

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A framework for adaptive open-pit mining planning under geological uncertainty

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OPTIMIZATION AND ENGINEERING 2022年第1期23卷 111-146页

作者： Lagos, Tomas Armstrong, Margaret Homem-de-Mello, Tito Lagos, Guido Saure, Denis Univ Chile Dept Ind Engn Santiago Chile Univ Adolfo Ibanez Sch Business Santiago Chile Univ Chile Ctr Math Modelling Santiago Chile Univ Adolfo Ibanez Fac Engn & Sci Santiago Chile

Mine planning optimization aims at maximizing the profit obtained from extracting valuable ore. Beyond its theoretical complexity-the open-pit mining problem with capacity constraints reduces to a knapsack problem with precedence constraints, which is NP-hard-practical instances of the problem usually involve a large to very large number of decision variables, typically of the order of millions for large mines. Additionally, any comprehensive approach to mine planning ought to consider the underlying geostatistical uncertainty as only limited information obtained from drill hole samples of the mineral is initially available. In this regard, as blocks are extracted sequentially, information about the ore grades of blocks yet to be extracted changes based on the blocks that have already been mined. Thus, the problem lies in the class of multi-period large scale stochastic optimization problems with decision-dependent information uncertainty. Such problems are exceedingly hard to solve, so approximations are required. This paper presents an adaptive optimization scheme for multi-period production scheduling in open-pit mining under geological uncertainty that allows us to solve practical instances of the problem. Our approach is based on a rolling-horizon adaptive optimization framework that learns from new information that becomes available as blocks are mined. By considering the evolution of geostatistical uncertainty, the proposed optimization framework produces an operational policy that reduces the risk of the production schedule. Our numerical tests with mines of moderate sizes show that our rolling horizon adaptive policy gives consistently better results than a non-adaptive stochastic optimization formulation, for a range of realistic problem instances.

关键词： Mine planning Geostatistics Stochastic optimization Adaptive algorithms iterative learning algorithm

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Hedging-point control policy for a failure-prone manufacturing system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2017年第8期231卷 1479-1487页

作者： Yan, Hong-Sen Jiang, Tian-Hua Meng, Xian-Gang Shi, Wen-Wu Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Tianjin Univ Technol Sch Automat Tianjin Peoples R China

The production control of failure-prone manufacturing systems is notoriously difficult because such systems are uncertain and non-linear. Since the introduction of hedging-point policies, many researches have been done in this field. However, there are few literatures that consider the production control problem of tree-structured manufacturing systems. In this article, a hedging-point production control policy is proposed for a multi-machine, tree-structured failure-prone manufacturing system. To obtain the optimal hedging points, an iterative learning algorithm is developed by considering the system's characteristics. A simulation method is embedded in the iterative learning algorithm to calculate the system cost. To estimate the performance of the proposed algorithm, comparisons are made between our algorithm, genetic algorithm and particle swarm optimization algorithm. The experimental results show that our algorithm works better than others in reducing the computation time and minimizing the production cost.

关键词： Tree-structured production control simulation method iterative learning algorithm

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Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2015年第1期23卷 1-7页

作者： Guo, Yuzhu Guo, Ling Zhong Billings, Stephen A. Wei, Hua-Liang Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Univ Sheffield INSIGNEO Inst In Silico Med Sheffield S1 3JD S Yorkshire England

A new iterative orthogonal least squares forward regression (iOFR) algorithm is proposed to identify nonlinear systems which may not be persistently excited. By slightly revising the classic forward orthogonal regression (OFR) algorithm, the new iterative algorithm provides search solutions on a global solution space. Examples show that the new iterative algorithm is computationally efficient and capable of producing a good model even when the input is not completely persistently excited.

关键词： model structure detection nonlinear system identification non-persistence orthogonal forward regression OFR iterative learning algorithm OFR algorithm iOFR algorithm

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Optimizing flight path accuracy of an autonomous quadrotor in windy conditions: integrated control strategies for tracking under perturbations and uncertainties

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第11期12卷 4120-4137页

作者： Abdelmaksoud, Sherif I. Mailah, Musa King Fahd Univ Petr & Minerals Aerosp Engn Dept Dhahran 31261 Saudi Arabia Univ Teknol Malaysia UTM Johor Bahru Sch Mech Engn Johor Baharu 81310 Johor Malaysia

Challenges and unpredictability pose significant barriers to maintaining stable operation in rotorcraft unmanned aerial vehicle (UAV) systems. The quadrotor model, as a type of rotorcraft UAV, is currently recognized as an exceptionally adaptable flying machine, serving various purposes in both civilian and military domains. However, it is a complex and highly non-linear system, and its effectiveness may suffer when subjected to external disturbances or uncertainties in its design. Using a technique known as active force control (AFC), novel intelligent control methods for quadrotors were presented in this study in order to enhance their ability to reject disturbances and uncertainties while maintaining system stability. To achieve this, a designed PID controller and an AFC technique were combined in a hybrid way into a single control strategy. To automatically estimate control parameters, the iterative learning algorithm (ILA), artificial neural network, and Adaptive Neuro-Fuzzy Inference System (ANFIS) were utilized, and the proposed control schemes became known as the PID-ILAFC, PID-NNAFC, and PID-ANFISAFC. To assess the effectiveness and resilience of the proposed control approaches, various perturbation representatives, including sinusoid and Dryden turbulence models, were employed along with uncertainties. The performance of the suggested control methods was evaluated using integral square error. Findings reveal an average decrease of over 55% in settling time across most scenarios. Concerning trajectory tracking accuracy, the integrated control strategies demonstrated remarkable efficacy in following the intended paths of the quadrotor, effectively mitigating the effects of applied wind gusts and uncertainties.

关键词： Rotorcraft UAVs Quadrotor Dryden wind gust Disturbance rejection PID Neural network ANFIS iterative learning algorithm Active force control Trajectory tracking

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Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning neural network control approach

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年第PartA期142卷

作者： Jia, Qingxian Gao, Junnan Zhang, Chengxi Ahn, Choon Ki Yu, Dan Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Korea Univ Sch Elect Engn Seoul 136701 South Korea

This paper addresses the robust attitude synchronization issue in a multi-spacecraft formation system subjected to limited communication, space disturbances, modeling uncertainties, and actuator faults. To accommodate limited inter-spacecraft communication, a dynamic event-triggered mechanism is designed to reduce the communication trigger frequency by dynamically adjusting the trigger threshold. Moreover, an event-based distributed self learning neural-network control (SLN2C) law is developed to guarantee robust attitude synchronization during multi-spacecraft formation. In the SLN2C scheme, a learning radial basis function neural network (RBFNN) model is proposed to online approximate and compensate for lumped disturbances, in which an iterative learning algorithm with a variable learning intensity is adopted to update the weight matrix of the RBFNN model. Compared with the traditional fixed learning intensity, a variable one can reduce initial oscillation and weaken the saturation response. Numerical simulations and comparisons are performed to illustrate the effectiveness and superiority of the proposed event-based spacecraft attitude synchronization control method.

关键词： Spacecraft formation Attitude synchronization Dynamic event-triggered learning neural-network control iterative learning algorithm

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Evolutionary algorithms for Self-tuning Active Vibration Control of Flexible Beam 3rd

Evolutionary Algorithms for Self-tuning Active Vibration Con...

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3rd Australian Control Conference (AUCC)

作者： Fadil, Muhammad Anas Darus, Intan Z. Mat Univ Teknol Malaysia Dept Appl Mech & Design Fac Mech Engn Johor Baharu Malaysia

ISBN: (纸本)9781479924981;9781479924974

This paper presents the development of self tuning Active Vibration Control (AVC) strategy for flexible beam structure. An experimental procedure was conducted on a flexible beam structure with clamped-free boundary condition. The beam was forced to vibrate using an external force and a set of input-output vibration data was acquired. Using the input-output data, the flexible beam model was developed using Least Squares (LS) algorithm that incorporated the Auto Regressive (ARX) model structure. The AVC controllers developed are proportional-derivative (PD) and proportional-integral-derivative (PID). The parameters of PD and PID controllers were tuned using iterative learning algorithm (ILA) and evolutionary Particle Swarm Optimization (PSO) techniques. Mean squared errors (MSE) were used to compare PSO tuned PD (PD-PSO), PSO tuned PID (PID-PSO) and PID with ILA (PID-ILA) controllers. It was found that the PID-ILA controller tuned using ILA had performed better than PID-PSO but PD-PSO is the best among the three controllers.

关键词： Flexible Beam iterative learning algorithm Least Square and Particle Swarm Optimization

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Upsetting machine control system based on the embedded technology

Upsetting machine control system based on the embedded techn...

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8th International Conference on Electronic Measurement and Instruments

作者： Wu Liming Lai Nanhui Wang Guitang Chen Jingyu Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)1424411351

In this paper, based on the electrothermal upsetting processing method, the process of valve upsetting is divided into two steps: initial upsetting and formal upsetting. Four parameters, including *** current;2 press;***;***, which influence the manufacture process in evidence, is mainly discussed. Analyzing defect and deficiency of tradition processing, the article, puts forward to a controlling strategy with iteration technique and designed an embedded valve upset forging microcomputer control system. The electrothermal upsetting control system based on embedded technology stores the experience and technique of local worker, optimizes the tradition processing by using ARM S3C2410X chip. During the process, controlling parameters such as press, displacement and voltage are real-time monitored. Signal feeds back to system by sensors to realize the closed loop controlling on-line self-study function. And the technics parameters and the controlling process will display on the HMI (Human Machine Interface). Using the system, the quality and efficiency of the valve manufacture are improved, and the robustness gets stronger. The system has been applied in enterprises. Comparing with the tradition one, the automaticity in manufacture is improved, and the rejection rate is reduced. It also benefits the informationization of production of enterprise.

关键词： electric upsetter control system iterative learning algorithm embedded system Vxworks

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Estimation of loss coefficients of Nonlinear rubber using iterative H_∞ filter

Estimation of loss coefficients of Nonlinear rubber using it...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chih-Hu Wang Lee, Bore-Kuen Wei-Hang Tseng Chung-Hsi Fu Chauchin Su Liu, Chien-Nan Jimmy Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan Chung Hua Univ Dept Elect Engn Hsinchu 300 Taiwan Chu Chang Rubber Co Ltd Hsinchu 30061 Taiwan Natl Chiao Tung Univ Dept ECE Hsinchu 300 Taiwan Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan

ISBN: (纸本)9781424400997

For automobile industry, rubber is widely used for noise isolation and vibration reduction. However, due to its distributed and nonlinear characteristics, it is hard to precisely estimate its characteristics such as the loss coefficient which is defined as the tangent of the phase delay between the fundamental components of the strain and the stress under sinusoidal driving. Moreover, even using a truncated finite-dimensional model, with rubber's nonlinearity, resonance of the testing mechanical system, and measurement noise, optimal estimation of the loss coefficient by using Kalman filter is not feasible in the presence of these uncertainties and non-Gaussian disturbances. Therefore, H-infinity filter is applied in this paper to robustly estimate the loss coefficient from the state-space perspective. As a state-space model for representing a sinusoidal signal has eigenvalues on the unit circle, the measured data is first processed by imposing a suitable exponential decay in order to ensure the stability of the H-infinity filter. Moreover, due to finite data length, an iterative H-infinity filter is developed to improve the accuracy of parameter estimates. At each iteration, estimation of disturbances by using the H-infinity filter is first performed by applying the previously estimated components of the desired signal. Then a robust estimation of the desired signal is made with respect to the measured signal which is subtracted by the estimated disturbance. Both simulation study and experimental test are conducted to verify the performance of the proposed iterative H. filter.

关键词： rubber H-infinity filter loss coefficient iterative learning algorithm

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