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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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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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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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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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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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iterative learning Fault Estimation algorithm for Time-delay Systems Based on Extended Observer 7

Iterative Learning Fault Estimation Algorithm for Time-delay...

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IEEE 7th Data Driven Control and learning Systems Conference (DDCLS)

作者： Tao, Hongfeng Wei, Qiang Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

ISBN: (纸本)9781538626184

For a class of multivariable linear, time-delay systems with actuator fault and measurement bounded disturbances in output, an iterative learning fault estimation algorithm based on extended observer is proposed. The extended observer is designed in terms of the linear matrix inequality technique such that the states and disturbances can be estimated simultaneously in every trials, then the faults and disturbances can be separated for avoiding impact to each other. Afterwards, the iterative learning fault estimation algorithm by defining estimation residual is chosen to adaptively approximate the actuator fault with initial error, then the necessary and sufficient conditions for the existence of the learning algorithm is given through. norm theory and Bellman-Gronwall inequality, and the uniform convergence criteria of the control algorithm is also discussed. Simulation results verify the feasibility and effectiveness of this algorithm.

关键词： Time-delay extended observer fault estimation iterative learning algorithm

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iterative learning Fault Diagnosis algorithm for Non-uniform Sampling Hybrid System

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 534-542页

作者： Hongfeng Tao Dapeng Chen Huizhong Yang Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122China

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is ***,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay ***,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults *** results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.

关键词： Equivalent fault model fault diagnosis iterative learning algorithm non-uniform sampling hybrid system virtual fault

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A gait rehabilitation strategy inspired by an iterative learning algorithm

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MECHATRONICS 2012年第2期22卷 213-221页

作者： Bae, Joonbum Tomizuka, Masayoshi UNIST Sch Mech & Adv Mat Engn Ulsan South Korea Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical therapists. In manual gait rehabilitation training, physical therapists have patients practice and memorize normal gait patterns by applying assistive torque to the patient's joint once the patient's gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient's joint during rehabilitation training. In this paper, the gait rehabilitation strategy inspired by an iterative learning algorithm is proposed, which uses the repetitive characteristic of gait motions. In the proposed strategy, the assistive joint torque in the current stride is calculated based on the information from previous strides. Simulation results and experimental results using an active knee orthosis are presented, which verify that the proposed strategy can be used to calculate appropriate assistive joint torque to excise the desired motions for rehabilitation. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Gait rehabilitation strategy iterative learning algorithm Robotic gait rehabilitation device

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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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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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